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Wu Q, Dai J, Liu J, Wu L. Bridging Genomic Research Disparities in Osteoporosis GWAS: Insights for Diverse Populations. Curr Osteoporos Rep 2025; 23:24. [PMID: 40411668 PMCID: PMC12103327 DOI: 10.1007/s11914-025-00917-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/23/2025] [Indexed: 05/26/2025]
Abstract
PURPOSE OF REVIEW Genome-wide association studies (GWAS) have significantly advanced osteoporosis research by identifying genetic loci associated with bone mineral density (BMD) and fracture risk. However, disparities persist due to the underrepresentation of non-European populations, limiting the applicability of polygenic risk scores (PRS). This review examines recent advancements in osteoporosis genetics, highlights existing disparities, and explores strategies for more inclusive research. RECENT FINDINGS European-focused GWAS have identified key loci for osteoporosis, including WNT signaling (SOST, LRP5) and RUNX2 transcriptional regulation. However, fewer than 40% of these variants can be replicated in Asian and African populations. Emerging studies in non-European groups reveal population-specific loci, sex-specific associations, and gene-environment interactions. Advances in machine learning (ML)-assisted GWAS and multi-omics integration are improving genetic discovery. Expanding GWAS in diverse populations, integrating multi-omics data, refining ML-based risk models, and standardizing biobank data are essential for equitable osteoporosis research. Future efforts must prioritize clinical translation to enhance personalized osteoporosis prevention and treatment.
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Affiliation(s)
- Qing Wu
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, 250 Lincoln Tower, 1800 Cannon Drive, Columbus, OH, 43210, USA.
| | - Jingyuan Dai
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, 250 Lincoln Tower, 1800 Cannon Drive, Columbus, OH, 43210, USA
| | - Jianing Liu
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, 250 Lincoln Tower, 1800 Cannon Drive, Columbus, OH, 43210, USA
| | - Lang Wu
- Pacific Center for Genome Research, University of Hawai'i at Mānoa, Honolulu, HI, USA
- Population Sciences in the Pacific Program, University of Hawai'i Cancer Center, University of Hawai'i at Mānoa, Honolulu, HI, USA
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2
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Xiang R, Zhao X, Sha L, Tang M, Wu X, Zhang L, Hou J, Deng Q, Qu Y, Zhu J, Qin C, Xiao C, Xiao J, Zhong Y, Yang B, Song X, Zhou J, Han T, Zheng S, Yu T, Liao J, Fan M, Li J, Liu Z, Jiang X. Mapping the role of macro and micronutrients in bone mineral density: a comprehensive Mendelian randomization study. Eur J Nutr 2025; 64:156. [PMID: 40240651 PMCID: PMC12003484 DOI: 10.1007/s00394-025-03665-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2024] [Accepted: 03/22/2025] [Indexed: 04/18/2025]
Abstract
BACKGROUND Macro and micronutrients may play an important role in osteoporosis development; however, observational studies have yielded inconsistent results. Clarifying these associations is vital to the development of nutritional recommendations aimed at preventing osteoporosis. METHODS Utilizing the largest available genome-wide association study (GWAS) summary statistics to date, we performed a two-sample Mendelian randomization (MR) analysis to investigate the causal effects of energy-adjusted macronutrient intake (fat, protein, carbohydrate, and sugar) and circulating levels of 20 micronutrients (ten each for vitamins and minerals) on heel estimated bone mineral density (eBMD), a promising marker for osteoporosis risk and fracture susceptibility. Sensitivity, sex-specific, and one-sample MR analyses were applied to further validate and annotate the results. RESULTS Among all nutrients, four genetically predicted circulating levels of micronutrients were suggestively associated with eBMD (vitamin A: β IVW = - 0.054, PIVW = 3.70 × 10-2; vitamin B12: β IVW = - 0.020, PIVW = 3.71 × 10-6; vitamin E: β IVW = 0.277, PIVW = 3.22 × 10-2; selenium: β IVW = 0.023, PIVW = 5.37 × 10-3; all Pintercept > 0.05). All these results were also considered robust, as sensitivity analyses yielded directionally consistent results. However, only the causal effects of vitamin B12 and selenium on eBMD remained significant after Bonferroni correction and were not confounded by obesity, smoking status, or alcohol consumption. Sex-specific analysis revealed a male-specific causal association between vitamin E and eBMD ( β IVW = 0.275, PIVW = 9.81 × 10-14). Additionally, using individuallevel data from the UK Biobank cohort, one-sample MR analysis found no causal relationships between diet-derived nutrient intake and eBMD in the overall population, as well as in the females or the males. CONCLUSIONS Our findings suggest that appropriate levels of plasma vitamin B12 and adequate levels of serum selenium are crucial for delaying bone loss and promoting bone health, emphasizing the need for nutritional recommendations to maintain optimal levels of these nutrients to promote eBMD and prevent osteoporosis.
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Affiliation(s)
- Rong Xiang
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xunying Zhao
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Linna Sha
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Mingshuang Tang
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xueyao Wu
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Li Zhang
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jiaojiao Hou
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Qin Deng
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yang Qu
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jiangbo Zhu
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Chenjiarui Qin
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Changfeng Xiao
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jinyu Xiao
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yangdan Zhong
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Bin Yang
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xin Song
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jinyu Zhou
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Tao Han
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Sirui Zheng
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Ting Yu
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jiaqiang Liao
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Mengyu Fan
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jiayuan Li
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Zhonghua Liu
- Department of Clinical Nutrition, No.4 West China Teaching Hospital, Sichuan University, Chengdu, Sichuan, People's Republic of China.
| | - Xia Jiang
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China.
- Department of Epidemiology and Health Statistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China.
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institutet, Solna, Stockholm, Sweden.
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Martínez-Gil N, Herrera-Ubeda C, Gritti N, Roca-Ayats N, Ugartondo N, Garcia-Giralt N, Ovejero D, Nogués X, Garcia-Fernàndez J, Grinberg D, Balcells S. Regulation of WNT16 in bone may involve upstream enhancers within CPED1. Sci Rep 2025; 15:9607. [PMID: 40113825 PMCID: PMC11926113 DOI: 10.1038/s41598-025-93259-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2024] [Accepted: 03/05/2025] [Indexed: 03/22/2025] Open
Abstract
WNT16 stands up as an essential gene for bone homeostasis. Here, we present new evidence of the functional role of a particular region within WNT16. Performing 4 C chromatin conformation analysis in three osteoblast-related cells (the human fetal osteoblast hFOB 1.19 cell line, Saos 2 osteosarcoma cell line and mesenchymal Stem Cells -MSC-), we identify physical interactions between the proximal part of WNT16 intron 2, shown here to be an active promoter in Saos 2 osteosarcoma cells, and several putative regulatory regions within CPED1. Analysis of previously published RNA-seq data from hFOB cells disclosed low expression of a region located downstream of this promoter. Our results suggest a novel regulatory mechanism of WNT16 in bone, mediated by physical interaction with various enhancer regions within CPED1.
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Affiliation(s)
- N Martínez-Gil
- Department of Genetics, Microbiology and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
- Institut de Biomedicina (IBUB), Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III , Madrid, Spain
- Institut de Recerca Sant Joan de Déu (IRSJD), Barcelona, Spain
| | - C Herrera-Ubeda
- Department of Genetics, Microbiology and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
- Institut de Biomedicina (IBUB), Universitat de Barcelona, Barcelona, Spain
| | - N Gritti
- European Molecular Biology Laboratory (EMBL) Barcelona, 08003, Barcelona, Spain
| | - N Roca-Ayats
- Department of Genetics, Microbiology and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
- Institut de Biomedicina (IBUB), Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III , Madrid, Spain
- Institut de Recerca Sant Joan de Déu (IRSJD), Barcelona, Spain
| | - N Ugartondo
- Department of Genetics, Microbiology and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
- Institut de Biomedicina (IBUB), Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III , Madrid, Spain
- Institut de Recerca Sant Joan de Déu (IRSJD), Barcelona, Spain
| | - N Garcia-Giralt
- Department of Genetics, Microbiology and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
- Musculoskeletal Research Group, Hospital del Mar Research Institute, Centro de Investigación Biomédica en Red en Fragilidad y Envejecimiento Saludable (CIBERFES), ISCIII, Barcelona, Spain
| | - D Ovejero
- Musculoskeletal Research Group, Hospital del Mar Research Institute, Centro de Investigación Biomédica en Red en Fragilidad y Envejecimiento Saludable (CIBERFES), ISCIII, Barcelona, Spain
| | - X Nogués
- Musculoskeletal Research Group, Hospital del Mar Research Institute, Centro de Investigación Biomédica en Red en Fragilidad y Envejecimiento Saludable (CIBERFES), ISCIII, Barcelona, Spain
| | - J Garcia-Fernàndez
- Department of Genetics, Microbiology and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain
- Institut de Biomedicina (IBUB), Universitat de Barcelona, Barcelona, Spain
| | - Daniel Grinberg
- Department of Genetics, Microbiology and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain.
- Institut de Biomedicina (IBUB), Universitat de Barcelona, Barcelona, Spain.
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III , Madrid, Spain.
- Institut de Recerca Sant Joan de Déu (IRSJD), Barcelona, Spain.
| | - Susanna Balcells
- Department of Genetics, Microbiology and Statistics, Faculty of Biology, Universitat de Barcelona, Barcelona, Spain.
- Institut de Biomedicina (IBUB), Universitat de Barcelona, Barcelona, Spain.
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III , Madrid, Spain.
- Institut de Recerca Sant Joan de Déu (IRSJD), Barcelona, Spain.
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Zhou B, Huang H, Ding Z, Luo K, Chen Y, Han Y, Pang W, Tang W, Chen L, Jin W, Ma G, Cao H. Unveiling two distinct osteolineage cell populations linked to age-related osteoporosis in adult mice through integrative single-cell analyses. Cell Mol Life Sci 2025; 82:106. [PMID: 40067455 PMCID: PMC11896952 DOI: 10.1007/s00018-025-05597-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2024] [Revised: 01/13/2025] [Accepted: 01/15/2025] [Indexed: 03/15/2025]
Abstract
The bone marrow microenvironment contains heterogeneous stromal cells, which are critical for bone remodeling and provide essential supportive roles for hematopoietic functions. Although the diversity of PDGFRα+β+ mesenchymal stromal/stem cells (MSCs) get consensus, the osteo-lineage cells (OLCs) that constitute the developmental trajectory of osteoblasts are largely remain unclear. Here, we construct a comprehensive atlas of stromal cell via performing integrative single cell analyses for 77 samples from 14 datasets. Besides previously defined Lepr+ BM-MSCs derived OLC1, we present a novel OLC2 with unique molecular signatures and differentiation pathway. Both OLC1 and OLC2 show significant polygenic association with bone mineral density (BMD), while extracellular matrix (ECM) proteins specifically expressed by OLC2 are particularly decreased in bone tissue of aged mice. Notably, OLC1 and OLC2 are consistently reduced in aged mice, which might be induced by the decreased expression of several lineage drivers. Collectively, our study presents a thorough prospect of OLCs in the bone marrow microenvironment and highlights their important roles in age-related osteoporosis.
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Affiliation(s)
- Bo Zhou
- Department of Biochemistry, School of Medicine, Southern University of Science and Technology, Guangdong Provincial Key Laboratory of Cell Microenvironment and Disease Research, Shenzhen Key Laboratory of Cell Microenvironment, Key University Laboratory of Metabolism and Health of Guangdong, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Hongwen Huang
- Department of Biology, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Zhen Ding
- Department of Biochemistry, School of Medicine, Southern University of Science and Technology, Guangdong Provincial Key Laboratory of Cell Microenvironment and Disease Research, Shenzhen Key Laboratory of Cell Microenvironment, Key University Laboratory of Metabolism and Health of Guangdong, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Kaiwen Luo
- Department of Biology, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Yangshan Chen
- Department of Biochemistry, School of Medicine, Southern University of Science and Technology, Guangdong Provincial Key Laboratory of Cell Microenvironment and Disease Research, Shenzhen Key Laboratory of Cell Microenvironment, Key University Laboratory of Metabolism and Health of Guangdong, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Yingying Han
- Department of Biochemistry, School of Medicine, Southern University of Science and Technology, Guangdong Provincial Key Laboratory of Cell Microenvironment and Disease Research, Shenzhen Key Laboratory of Cell Microenvironment, Key University Laboratory of Metabolism and Health of Guangdong, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Wei Pang
- Department of Biochemistry, School of Medicine, Southern University of Science and Technology, Guangdong Provincial Key Laboratory of Cell Microenvironment and Disease Research, Shenzhen Key Laboratory of Cell Microenvironment, Key University Laboratory of Metabolism and Health of Guangdong, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Wanze Tang
- Department of Biochemistry, School of Medicine, Southern University of Science and Technology, Guangdong Provincial Key Laboratory of Cell Microenvironment and Disease Research, Shenzhen Key Laboratory of Cell Microenvironment, Key University Laboratory of Metabolism and Health of Guangdong, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Litong Chen
- Department of Biochemistry, School of Medicine, Southern University of Science and Technology, Guangdong Provincial Key Laboratory of Cell Microenvironment and Disease Research, Shenzhen Key Laboratory of Cell Microenvironment, Key University Laboratory of Metabolism and Health of Guangdong, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Wenfei Jin
- Shanghai Institute of Nutrition and Health, CAS, Shanghai, 200031, China.
| | - Guixing Ma
- Department of Biochemistry, School of Medicine, Southern University of Science and Technology, Guangdong Provincial Key Laboratory of Cell Microenvironment and Disease Research, Shenzhen Key Laboratory of Cell Microenvironment, Key University Laboratory of Metabolism and Health of Guangdong, Southern University of Science and Technology, Shenzhen, 518055, China.
| | - Huiling Cao
- Department of Biochemistry, School of Medicine, Southern University of Science and Technology, Guangdong Provincial Key Laboratory of Cell Microenvironment and Disease Research, Shenzhen Key Laboratory of Cell Microenvironment, Key University Laboratory of Metabolism and Health of Guangdong, Southern University of Science and Technology, Shenzhen, 518055, China.
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Di D, Zhou H, Cui Z, Zhang J, Liu Q, Yuan T, Zhou T, Luo X, Ling D, Wang Q. Early-life tobacco smoke elevating later-life osteoporosis risk: Mediated by telomere length and interplayed with genetic predisposition. J Adv Res 2025; 68:331-340. [PMID: 38431123 PMCID: PMC11785556 DOI: 10.1016/j.jare.2024.02.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 02/11/2024] [Accepted: 02/28/2024] [Indexed: 03/05/2024] Open
Abstract
INTRODUCTION The growing prevalence of osteoporosis (OP) in an aging global population presents a significant public health concern. Tobacco smoke negatively affects bone turnover, leading to reduced bone mass and heightened OP and fracture risk. However, the impact of early-life tobacco smoke exposure on later-life OP risk remains unclear. OBJECTIVES This study was to explore the effects of early-life tobacco smoke exposure on incident OP risk in later life. The mediating role of telomere length (TL) and the interaction with genetic predisposition were also studied. METHODS Data on in utero tobacco smoke exposure (IUTSE) status and age of tobacco use initiation from the UK Biobank were used to estimate early-life tobacco smoke exposure. Incident OP cases were identified according to health-related records. Linear, Cox, and Laplace regression models were mainly used for data analysis. RESULTS Individuals with IUTSE showed a higher OP risk [hazard ratio (HR): 1.06, 95 % confidence interval (CI): 1.01, 1.11] and experienced earlier OP onset by 0.30 years [50th percentile difference = -0.30, 95 % CI: -0.51, -0.09] compared to those without. Participants initiating tobacco smoke in childhood, adolescence, and adulthood had 1.41 times (95 % CI: 1.23, 1.61), 1.17 times (95 % CI:1.10, 1.24), and 1.14 times (95 % CI: 1.07, 1.20) the risk of OP, respectively, compared to never smokers. They also experienced earlier OP onset by 2.16, 0.95, and 0.71 years, sequentially. The TL significantly mediated the early-life tobacco exposure and OP association. Significant joint and interactive effects were detected between early-life tobacco smoke exposure and genetic elements. CONCLUSIONS Our findings implicate that early-life tobacco smoke exposure elevates the later-life OP risk, mediated by telomere length and interplayed with genetic predisposition. These findings highlight the importance of early-life intervention against tobacco smoke exposure and ageing status for precise OP prevention, especially in individuals with a high genetic risk.
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Affiliation(s)
- Dongsheng Di
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Haolong Zhou
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Zhangbo Cui
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Jianli Zhang
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Qian Liu
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Tingting Yuan
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Tingting Zhou
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Xiao Luo
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Danyang Ling
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Qi Wang
- Key Laboratory of Environment and Health, Ministry of Education & Ministry of Environmental Protection, Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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Conery M, Pippin JA, Wagley Y, Trang K, Pahl MC, Villani DA, Favazzo LJ, Ackert-Bicknell CL, Zuscik MJ, Katsevich E, Wells AD, Zemel BS, Voight BF, Hankenson KD, Chesi A, Grant SF. GWAS-Informed data integration and non-coding CRISPRi screen illuminate genetic etiology of bone mineral density. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.19.585778. [PMID: 38562830 PMCID: PMC10983984 DOI: 10.1101/2024.03.19.585778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Over 1,100 independent signals have been identified with genome-wide association studies (GWAS) for bone mineral density (BMD), a key risk factor for mortality-increasing fragility fractures; however, the effector gene(s) for most remain unknown. Informed by a variant-to-gene mapping strategy implicating 89 non-coding elements predicted to regulate osteoblast gene expression at BMD GWAS loci, we executed a single-cell CRISPRi screen in human fetal osteoblasts (hFOBs). The BMD relevance of hFOBs was supported by heritability enrichment from stratified LD-score regression involving 98 cell types grouped into 15 tissues. 23 genes showed perturbation in the screen, with four (ARID5B, CC2D1B, EIF4G2, and NCOA3) exhibiting consistent effects upon siRNA knockdown on three measures of osteoblast maturation and mineralization. Lastly, additional heritability enrichments, genetic correlations, and multi-trait fine-mapping revealed unexpectedly that many BMD GWAS signals are pleiotropic and likely mediate their effects via non-bone tissues. Extending our CRISPRi screening approach to these tissues could play a key role in fully elucidating the etiology of BMD.
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Affiliation(s)
- Mitchell Conery
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - James A. Pippin
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Yadav Wagley
- Department of Orthopaedic Surgery, University of Michigan Medical School, Ann Arbor, MI 48109
| | - Khanh Trang
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Matthew C. Pahl
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - David A. Villani
- Colorado Program for Musculoskeletal Research, University of Colorado Anschutz Medical Campus, Aurora, CO
- Cell Biology, Stems Cells and Development Ph.D. Program, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Lacey J. Favazzo
- Colorado Program for Musculoskeletal Research, University of Colorado Anschutz Medical Campus, Aurora, CO
- Department of Orthopedics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- University of Colorado Interdisciplinary Joint Biology Program, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Cheryl L. Ackert-Bicknell
- Colorado Program for Musculoskeletal Research, University of Colorado Anschutz Medical Campus, Aurora, CO
- Department of Orthopedics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- University of Colorado Interdisciplinary Joint Biology Program, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Michael J. Zuscik
- Colorado Program for Musculoskeletal Research, University of Colorado Anschutz Medical Campus, Aurora, CO
- Department of Orthopedics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
- University of Colorado Interdisciplinary Joint Biology Program, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Eugene Katsevich
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Andrew D. Wells
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Babette S. Zemel
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Division of Gastroenterology, Hepatology and Nutrition, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Benjamin F. Voight
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute of Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kurt D. Hankenson
- Department of Orthopaedic Surgery, University of Michigan Medical School, Ann Arbor, MI 48109
| | - Alessandra Chesi
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Struan F.A. Grant
- Center for Spatial and Functional Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Division of Human Genetics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute of Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Division of Endocrinology and Diabetes, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
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7
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Shine BK, Choi JE, Park YJ, Hong KW. The Genetic Variants Influencing Hypertension Prevalence Based on the Risk of Insulin Resistance as Assessed Using the Metabolic Score for Insulin Resistance (METS-IR). Int J Mol Sci 2024; 25:12690. [PMID: 39684400 DOI: 10.3390/ijms252312690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2024] [Revised: 11/24/2024] [Accepted: 11/25/2024] [Indexed: 12/18/2024] Open
Abstract
Insulin resistance is a major indicator of cardiovascular diseases, including hypertension. The Metabolic Score for Insulin Resistance (METS-IR) offers a simplified and cost-effective way to evaluate insulin resistance. This study aimed to identify genetic variants associated with the prevalence of hypertension stratified by METS-IR score levels. Data from the Korean Genome and Epidemiology Study (KoGES) were analyzed. The METS-IR was calculated using the following formula: ln [(2 × fasting blood glucose (FBG) + triglycerides (TG)) × body mass index (BMI)]/ ln [high-density lipoprotein cholesterol (HDL-C)]. The participants were divided into tertiles 1 (T1) and 3 (T3) based on their METS-IR scores. Genome-wide association studies (GWAS) were performed for hypertensive cases and non-hypertensive controls within these tertile groups using logistic regression adjusted for age, sex, and lifestyle factors. Among the METS-IR tertile groups, 3517 of the 19,774 participants (17.8%) at T1 had hypertension, whereas 8653 of the 20,374 participants (42.5%) at T3 had hypertension. A total of 113 single-nucleotide polymorphisms (SNPs) reached the GWAS significance threshold (p < 5 × 10-8) in at least one tertile group, mapping to six distinct genetic loci. Notably, four loci, rs11899121 (chr2p24), rs7556898 (chr2q24.3), rs17249754 (ATP2B1), and rs1980854 (chr20p12.2), were significantly associated with hypertension in the high-METS-score group (T3). rs10857147 (FGF5) was significant in both the T1 and T3 groups, whereas rs671 (ALDH2) was significant only in the T1 group. The GWASs identified six genetic loci significantly associated with hypertension, with distinct patterns across METS-IR tertiles, highlighting the role of metabolic context in genetic susceptibility. These findings underscore critical genetic factors influencing hypertension prevalence and provide insights into the metabolic-genetic interplay underlying this condition.
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Affiliation(s)
- Bo-Kyung Shine
- Department of Family Medicine, Medical Center, Dong-A University, Busan 49201, Republic of Korea
| | - Ja-Eun Choi
- Institute of Advanced Technology, Theragen Health Co., Ltd., Seongnam 13493, Republic of Korea
| | - Young-Jin Park
- Department of Family Medicine, Medical Center, Dong-A University, Busan 49201, Republic of Korea
| | - Kyung-Won Hong
- Institute of Advanced Technology, Theragen Health Co., Ltd., Seongnam 13493, Republic of Korea
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8
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Fan Y, Chen J, Fan Z, Chirinos J, Stein JL, Sullivan PF, Wang R, Nadig A, Zhang DY, Huang S, Jiang Z, Guan PY, Qian X, Li T, Li H, Sun Z, Ritchie MD, O’Brien J, Witschey W, Rader DJ, Li T, Zhu H, Zhao B. Mapping rare protein-coding variants on multi-organ imaging traits. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.11.16.24317443. [PMID: 39606337 PMCID: PMC11601754 DOI: 10.1101/2024.11.16.24317443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/29/2024]
Abstract
Human organ structure and function are important endophenotypes for clinical outcomes. Genome-wide association studies (GWAS) have identified numerous common variants associated with phenotypes derived from magnetic resonance imaging (MRI) of the brain and body. However, the role of rare protein-coding variations affecting organ size and function is largely unknown. Here we present an exome-wide association study that evaluates 596 multi-organ MRI traits across over 50,000 individuals from the UK Biobank. We identified 107 variant-level associations and 224 gene-based burden associations (67 unique gene-trait pairs) across all MRI modalities, including PTEN with total brain volume, TTN with regional peak circumferential strain in the heart left ventricle, and TNFRSF13B with spleen volume. The singleton burden model and AlphaMissense annotations contributed 8 unique gene-trait pairs including the association between an approved drug target gene of KCNA5 and brain functional activity. The identified rare coding signals elucidate some shared genetic regulation across organs, prioritize previously identified GWAS loci, and are enriched for drug targets. Overall, we demonstrate how rare variants enhance our understanding of genetic effects on human organ morphology and function and their connections to complex diseases.
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Affiliation(s)
- Yijun Fan
- Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jie Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Zirui Fan
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Julio Chirinos
- Division of Cardiovascular Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Jason L. Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Patrick F. Sullivan
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Rujin Wang
- Regeneron Genetics Center, 777 Old Saw Mill River Rd., Tarrytown, NY, 10591, USA
| | - Ajay Nadig
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - David Y. Zhang
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Shuai Huang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Zhiwen Jiang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Peter Yi Guan
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Xinjie Qian
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Ting Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Haoyue Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Zehui Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Marylyn D. Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania Philadelphia, PA 19104, USA
| | - Joan O’Brien
- Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Medicine Center for Ophthalmic Genetics in Complex Diseases, Philadelphia, PA 19104, USA
| | - Walter Witschey
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Daniel J. Rader
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Tengfei Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Bingxin Zhao
- Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania Philadelphia, PA 19104, USA
- Center for AI and Data Science for Integrated Diagnostics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Population Aging Research Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Center for Eye-Brain Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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9
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Chermside-Scabbo CJ, Shuster JT, Erdmann-Gilmore P, Tycksen E, Zhang Q, Townsend RR, Silva MJ. A proteomics approach to study mouse long bones: examining baseline differences and mechanical loading-induced bone formation in young-adult and old mice. Aging (Albany NY) 2024; 16:12726-12768. [PMID: 39400554 PMCID: PMC11501390 DOI: 10.18632/aging.206131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 09/23/2024] [Indexed: 10/15/2024]
Abstract
With aging, bone mass declines and the anabolic effects of skeletal loading diminish. While much research has focused on gene transcription, how bone ages and loses its mechanoresponsiveness at the protein level remains unclear. We developed a novel proteomics approach and performed a paired mass spectrometry and RNA-seq analysis on tibias from young-adult (5-month) and old (22-month) mice. We report the first correlation estimate between the bone proteome and transcriptome (Spearman ρ = 0.40), which is in line with other tissues but indicates that a relatively low amount of variation in protein levels is explained by the variation in transcript levels. Of 71 shared targets that differed with age, eight were associated with bone mineral density in previous GWAS, including understudied targets Asrgl1 and Timp2. We used complementary RNA in situ hybridization to confirm that Asrgl1 and Timp2 had reduced expression in osteoblasts/osteocytes in old bones. We also found evidence for reduced TGF-beta signaling with aging, in particular Tgfb2. Next, we defined proteomic changes following mechanical loading. At the protein level, bone differed more with age than with loading, and aged bone had fewer loading-induced changes. Overall, our findings underscore the need for complementary protein-level assays in skeletal biology research.
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Affiliation(s)
- Christopher J. Chermside-Scabbo
- Department of Orthopaedic Surgery, Washington University School of Medicine, St. Louis, MO 63110, USA
- Medical Scientist Training Program, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - John T. Shuster
- Department of Orthopaedic Surgery, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Petra Erdmann-Gilmore
- Department of Medicine, Proteomics Core, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Eric Tycksen
- Department of Genetics, McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Qiang Zhang
- Department of Medicine, Proteomics Core, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - R. Reid Townsend
- Department of Medicine, Proteomics Core, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Matthew J. Silva
- Department of Orthopaedic Surgery, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Biomedical Engineering, Washington University, St. Louis, MO 63105, USA
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10
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Qin C, Zhang W, Xiao C, Qu Y, Xiao J, Wu X, Zhang L, Wang Y, He L, Zhu J, Wang W, Li Y, Sun L, Jiang X. Shared genetic basis connects smoking behaviors and bone health: insights from a genome-wide cross-trait analysis. J Bone Miner Res 2024; 39:918-928. [PMID: 38843381 PMCID: PMC12102605 DOI: 10.1093/jbmr/zjae082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 04/09/2024] [Accepted: 05/13/2024] [Indexed: 08/07/2024]
Abstract
Although the negative association of tobacco smoking with osteoporosis is well-documented, little is known regarding the shared genetic basis underlying these conditions. In this study, we aim to investigate a shared genetic architecture between smoking and heel estimated bone mineral density (eBMD), a reliable proxy for osteoporosis. We conducted a comprehensive genome-wide cross-trait analysis to identify genetic correlation, pleiotropic loci and causal relationship of smoking with eBMD, leveraging summary statistics of the hitherto largest genome-wide association studies conducted in European ancestry for smoking initiation (Nsmoker = 1 175 108, Nnonsmoker = 1 493 921), heaviness (cigarettes per day, N = 618 489), cessation (Ncurrent smoker = 304 244, Nformer smoker = 843 028), and eBMD (N = 426 824). A significant negative global genetic correlation was found for smoking cessation and eBMD (${r}_g$ = -0.051, P = 0.01), while we failed to identify a significant global genetic correlation of smoking initiation or heaviness with eBMD. Partitioning the whole genome into independent blocks, we observed 6 significant shared local signals for smoking and eBMD, with 22q13.1 showing the strongest regional genetic correlation. Such a genetic overlap was further supported by 71 pleiotropic loci identified in the cross-trait meta-analysis. Mendelian randomization identified no causal effect of smoking initiation (beta = -0.003 g/cm2, 95% CI = -0.033 to 0.027) or heaviness (beta = -0.017 g/cm2, 95% CI = -0.072 to 0.038) on eBMD, but a putative causal effect of genetic predisposition to being a current smoker was associated with a lower eBMD compared to former smokers (beta = -0.100 g/cm2, 95% CI = -0.181 to -0.018). Our study demonstrates a pronounced biological pleiotropy as well as a putative causal link between current smoking status and eBMD, providing novel insights into the primary prevention and modifiable intervention of osteoporosis by advocating individuals to avoid, reduce or quit smoking as early as possible.
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Affiliation(s)
- Chenjiarui Qin
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Wenqiang Zhang
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Changfeng Xiao
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Yang Qu
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Jinyu Xiao
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Xueyao Wu
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
- National Cancer Institute, Rockville, MD 610041, United States
| | - Li Zhang
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Yutong Wang
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Lin He
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Jingwei Zhu
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Wenzhi Wang
- Department of Osteoporosis/Rheumatology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Yun Li
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Lei Sun
- Department of Osteoporosis/Rheumatology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
| | - Xia Jiang
- Department of Nutrition and Food Hygiene, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, 610041, China
- Department of Clinical Neuroscience, Center for Molecular Medicine, Karolinska Institute, Solna, Stockholm, 17177, Sweden
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11
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Ibragimov E, Pedersen AØ, Sloth NM, Fredholm M, Karlskov-Mortensen P. Identification of a novel QTL for lean meat percentage using imputed genotypes. Anim Genet 2024; 55:658-663. [PMID: 38752377 DOI: 10.1111/age.13442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 04/16/2024] [Accepted: 04/24/2024] [Indexed: 07/04/2024]
Abstract
Lean meat percentage is a critical production trait in pig breeding systems with direct implications for the sustainability of the industry. In this study, we conducted a genome-wide association study for lean meat percentage using a cohort of 850 Duroc × (Landrace × Yorkshire) crossbred pigs and we identified QTL on SSC3 and SSC18. Based on the predicted effect of imputed variants and using the PigGTEx database of molecular QTL, we prioritized candidate genes and SNPs located within the QTL regions, which may be involved in the regulation of porcine leanness. Our results indicate that a nonsense mutation in ZC3HAV1L on SSC18 has a direct effect on lean meat percentage.
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Affiliation(s)
- Emil Ibragimov
- Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg, Denmark
| | - Anni Øyan Pedersen
- Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg, Denmark
| | | | - Merete Fredholm
- Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg, Denmark
| | - Peter Karlskov-Mortensen
- Department of Veterinary and Animal Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Frederiksberg, Denmark
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12
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Yuan K, Xie X, Huang W, Li D, Zhao Y, Yang H, Wang X. Elucidating causal relationships of diet-derived circulating antioxidants and the risk of osteoporosis: A Mendelian randomization study. Front Genet 2024; 15:1346367. [PMID: 38911297 PMCID: PMC11190308 DOI: 10.3389/fgene.2024.1346367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 05/24/2024] [Indexed: 06/25/2024] Open
Abstract
Background Osteoporosis (OP) is typically diagnosed by evaluating bone mineral density (BMD), and it frequently results in fractures. Here, we investigated the causal relationships between diet-derived circulating antioxidants and the risk of OP using Mendelian randomization (MR). Methods Published studies were used to identify instrumental variables related to absolute levels of circulating antioxidants like lycopene, retinol, ascorbate, and β-carotene, as well as antioxidant metabolites such as ascorbate, retinol, α-tocopherol, and γ-tocopherol. Outcome variables included BMD (in femoral neck, lumbar spine, forearm, heel, total body, total body (age over 60), total body (age 45-60), total body (age 30-45), total body (age 15-30), and total body (age 0-15)), fractures (in arm, spine, leg, heel, and osteoporotic fractures), and OP. Inverse variance weighted or Wald ratio was chosen as the main method for MR analysis based on the number of single nucleotide polymorphisms (SNPs). Furthermore, we performed sensitivity analyses to confirm the reliability of the findings. Results We found a causal relationship between absolute retinol levels and heel BMD (p = 7.6E-05). The results of fixed effects IVW showed a protective effect of absolute retinol levels against heel BMD, with per 0.1 ln-transformed retinol being associated with a 28% increase in heel BMD (OR: 1.28, 95% CI: 1.13-1.44). In addition, a sex-specific effect of the absolute circulating retinol levels on the heel BMD has been observed in men. No other significant causal relationship was found. Conclusion There is a positive causal relationship between absolute retinol levels and heel BMD. The implications of our results should be taken into account in future studies and in the creation of public health policies and OP prevention tactics.
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Affiliation(s)
- Kexin Yuan
- Gansu University of Chinese Medicine, Lanzhou, China
| | - Xingwen Xie
- Affiliated Hospital of Gansu University of Chinese Medicine, Lanzhou, China
| | - Weiwei Huang
- Gansu University of Chinese Medicine, Lanzhou, China
| | - Dingpeng Li
- The Second People’s Hospital of Gansu Province, Lanzhou, China
| | - Yongli Zhao
- Affiliated Hospital of Gansu University of Chinese Medicine, Lanzhou, China
| | - Haodong Yang
- Gansu University of Chinese Medicine, Lanzhou, China
| | - Xuetao Wang
- Gansu University of Chinese Medicine, Lanzhou, China
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13
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Busse E, Lee B, Nagamani SCS. Genetic Evaluation for Monogenic Disorders of Low Bone Mass and Increased Bone Fragility: What Clinicians Need to Know. Curr Osteoporos Rep 2024; 22:308-317. [PMID: 38600318 DOI: 10.1007/s11914-024-00870-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/23/2024] [Indexed: 04/12/2024]
Abstract
PURPOSE OF REVIEW The purpose of this review is to outline the principles of clinical genetic testing and to provide practical guidance to clinicians in navigating genetic testing for patients with suspected monogenic forms of osteoporosis. RECENT FINDINGS Heritability assessments and genome-wide association studies have clearly shown the significant contributions of genetic variations to the pathogenesis of osteoporosis. Currently, over 50 monogenic disorders that present primarily with low bone mass and increased risk of fractures have been described. The widespread availability of clinical genetic testing offers a valuable opportunity to correctly diagnose individuals with monogenic forms of osteoporosis, thus instituting appropriate surveillance and treatment. Clinical genetic testing may identify the appropriate diagnosis in a subset of patients with low bone mass, multiple or unusual fractures, and severe or early-onset osteoporosis, and thus clinicians should be aware of how to incorporate such testing into their clinical practices.
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Affiliation(s)
- Emily Busse
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Medical Scientist Training Program, Baylor College of Medicine, Houston, TX, USA
| | - Brendan Lee
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
- Texas Children's Hospital, Houston, TX, USA.
| | - Sandesh C S Nagamani
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Texas Children's Hospital, Houston, TX, USA
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14
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Gao Y, Zhang X, Chen H, Lu Y, Ma S, Yang Y, Zhang M, Xu S. Reconstructing the ancestral gene pool to uncover the origins and genetic links of Hmong-Mien speakers. BMC Biol 2024; 22:59. [PMID: 38475771 PMCID: PMC10935854 DOI: 10.1186/s12915-024-01838-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 02/01/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND Hmong-Mien (HM) speakers are linguistically related and live primarily in China, but little is known about their ancestral origins or the evolutionary mechanism shaping their genomic diversity. In particular, the lack of whole-genome sequencing data on the Yao population has prevented a full investigation of the origins and evolutionary history of HM speakers. As such, their origins are debatable. RESULTS Here, we made a deep sequencing effort of 80 Yao genomes, and our analysis together with 28 East Asian populations and 968 ancient Asian genomes suggested that there is a strong genetic basis for the formation of the HM language family. We estimated that the most recent common ancestor dates to 5800 years ago, while the genetic divergence between the HM and Tai-Kadai speakers was estimated to be 8200 years ago. We proposed that HM speakers originated from the Yangtze River Basin and spread with agricultural civilization. We identified highly differentiated variants between HM and Han Chinese, in particular, a deafness-related missense variant (rs72474224) in the GJB2 gene is in a higher frequency in HM speakers than in others. CONCLUSIONS Our results indicated complex gene flow and medically relevant variants involved in the HM speakers' evolution history.
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Affiliation(s)
- Yang Gao
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Xiaoxi Zhang
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Hao Chen
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yan Lu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Sen Ma
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yajun Yang
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, China
| | - Menghan Zhang
- Institute of Modern Languages and Linguistics, and Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, 200433, China
| | - Shuhua Xu
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Center for Evolutionary Biology, Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China.
- Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, China.
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15
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Wang Y, Huang X, Zhang Q, Cheng C, Qin Z, Lu L, Huang Q. The osteoporosis susceptibility SNP rs188303909 at 2q14.2 regulates EN1 expression by modulating DNA methylation and E2F6 binding. J Mol Med (Berl) 2024; 102:273-284. [PMID: 38153509 DOI: 10.1007/s00109-023-02412-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 12/11/2023] [Accepted: 12/19/2023] [Indexed: 12/29/2023]
Abstract
EN1 encodes a homeodomain-containing transcription factor and is a determinant of bone density and fracture. Previous powerful genome-wide association studies (GWASs) have identified multiple single-nucleotide polymorphisms (SNPs) near EN1 at 2q14.2 locus for osteoporosis, but the causal SNPs and functional mechanisms underlying these associations are poorly understood. The target genes regulated by the transcription factor EN1 are also unclear. In this study, we identified rs188303909, a functional CpG-SNP, as a causal SNP for osteoporosis at 2q14.2 through the integration of functional and epigenomic analyses. Functional experiments demonstrated that unmethylated rs188303909 acted as a strong allele-specific distal enhancer to regulate EN1 expression by modifying the binding of transcription factor E2F6, but rs188303909 methylation attenuated the active effect of E2F6 on EN1 expression. Importantly, transcription factor EN1 could differentially bind osteoporosis GWAS lead SNPs rs4869739-T and rs4355801-G to upregulate CCDC170 and COLEC10 expression, thus promoting bone formation. Our study provided a mechanistic insight into expression regulation of the osteoporosis susceptibility gene EN1, which could be a potential therapeutic target for osteoporosis precision medicine. KEY MESSAGES: CpG-SNP rs188303909 is a causal SNP at the osteoporosis susceptibility locus 2q14.2. Rs188303909 distally regulates EN1 expression by modulating DNA methylation and E2F6 binding. EN1 upregulates CCDC170 and COLEC10 expression through osteoporosis GWAS lead SNPs rs4869739 and rs4355801.
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Affiliation(s)
- Ya Wang
- Hubei Key Laboratory of Genetic Regulation and Integrative Biology, School of Life Sciences, Central China Normal University, Wuhan, Hubei, 430079, China
- Key Laboratory of Laboratory Medicine, Ministry of Education, Zhejiang Provincial Key Laboratory of Medical Genetics, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China
| | - Xinyao Huang
- Hubei Key Laboratory of Genetic Regulation and Integrative Biology, School of Life Sciences, Central China Normal University, Wuhan, Hubei, 430079, China
| | - Qiongdan Zhang
- Hubei Key Laboratory of Genetic Regulation and Integrative Biology, School of Life Sciences, Central China Normal University, Wuhan, Hubei, 430079, China
| | - Chen Cheng
- Hubei Key Laboratory of Genetic Regulation and Integrative Biology, School of Life Sciences, Central China Normal University, Wuhan, Hubei, 430079, China
| | - Zixuan Qin
- Hubei Key Laboratory of Genetic Regulation and Integrative Biology, School of Life Sciences, Central China Normal University, Wuhan, Hubei, 430079, China
| | - Li Lu
- Hubei Key Laboratory of Genetic Regulation and Integrative Biology, School of Life Sciences, Central China Normal University, Wuhan, Hubei, 430079, China
| | - Qingyang Huang
- Hubei Key Laboratory of Genetic Regulation and Integrative Biology, School of Life Sciences, Central China Normal University, Wuhan, Hubei, 430079, China.
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16
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Brazill JM, Shen IR, Craft CS, Magee KL, Park JS, Lorenz M, Strickland A, Wee NK, Zhang X, Beeve AT, Meyer GA, Milbrandt J, DiAntonio A, Scheller EL. Sarm1 knockout prevents type 1 diabetic bone disease in females independent of neuropathy. JCI Insight 2024; 9:e175159. [PMID: 38175722 PMCID: PMC11143934 DOI: 10.1172/jci.insight.175159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 01/03/2024] [Indexed: 01/05/2024] Open
Abstract
Patients with diabetes have a high risk of developing skeletal diseases accompanied by diabetic peripheral neuropathy (DPN). In this study, we isolated the role of DPN in skeletal disease with global and conditional knockout models of sterile-α and TIR-motif-containing protein-1 (Sarm1). SARM1, an NADase highly expressed in the nervous system, regulates axon degeneration upon a range of insults, including DPN. Global knockout of Sarm1 prevented DPN, but not skeletal disease, in male mice with type 1 diabetes (T1D). Female wild-type mice also developed diabetic bone disease but without DPN. Unexpectedly, global Sarm1 knockout completely protected female mice from T1D-associated bone suppression and skeletal fragility despite comparable muscle atrophy and hyperglycemia. Global Sarm1 knockout rescued bone health through sustained osteoblast function with abrogation of local oxidative stress responses. This was independent of the neural actions of SARM1, as beneficial effects on bone were lost with neural conditional Sarm1 knockout. This study demonstrates that the onset of skeletal disease occurs rapidly in both male and female mice with T1D completely independently of DPN. In addition, this reveals that clinical SARM1 inhibitors, currently being developed for treatment of neuropathy, may also have benefits for diabetic bone through actions outside of the nervous system.
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Affiliation(s)
| | - Ivana R. Shen
- Division of Bone and Mineral Diseases, Department of Medicine, and
| | | | | | - Jay S. Park
- Division of Bone and Mineral Diseases, Department of Medicine, and
| | - Madelyn Lorenz
- Division of Bone and Mineral Diseases, Department of Medicine, and
| | - Amy Strickland
- Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Natalie K. Wee
- Division of Bone and Mineral Diseases, Department of Medicine, and
| | - Xiao Zhang
- Division of Bone and Mineral Diseases, Department of Medicine, and
- Department of Biomedical Engineering, McKelvey School of Engineering, Washington University, St. Louis, Missouri, USA
| | - Alec T. Beeve
- Division of Bone and Mineral Diseases, Department of Medicine, and
- Department of Biomedical Engineering, McKelvey School of Engineering, Washington University, St. Louis, Missouri, USA
| | | | - Jeffrey Milbrandt
- Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, USA
| | | | - Erica L. Scheller
- Division of Bone and Mineral Diseases, Department of Medicine, and
- Department of Biomedical Engineering, McKelvey School of Engineering, Washington University, St. Louis, Missouri, USA
- Department of Developmental Biology, and
- Department of Cell Biology and Physiology, Washington University School of Medicine, St. Louis, Missouri, USA
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17
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Ahles A, Engelhardt S. Genetic Variants of Adrenoceptors. Handb Exp Pharmacol 2024; 285:27-54. [PMID: 37578621 DOI: 10.1007/164_2023_676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Adrenoceptors are class A G-protein-coupled receptors grouped into three families (α1-, α2-, and β-adrenoceptors), each one including three members. All nine corresponding adrenoceptor genes display genetic variation in their coding and adjacent non-coding genomic region. Coding variants, i.e., nucleotide exchanges within the transcribed and translated receptor sequence, may result in a difference in amino acid sequence thus altering receptor function and signaling. Such variants have been intensely studied in vitro in overexpression systems and addressed in candidate-gene studies for distinct clinical parameters. In recent years, large cohorts were analyzed in genome-wide association studies (GWAS), where variants are detected as significant in context with specific traits. These studies identified two of the in-depth characterized 18 coding variants in adrenoceptors as repeatedly statistically significant genetic risk factors - p.Arg389Gly in the β1- and p.Thr164Ile in the β2-adrenoceptor, along with 56 variants in the non-coding regions adjacent to the adrenoceptor gene loci, the functional role of which is largely unknown at present. This chapter summarizes current knowledge on the two coding variants in adrenoceptors that have been consistently validated in GWAS and provides a prospective overview on the numerous non-coding variants more recently attributed to adrenoceptor gene loci.
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Affiliation(s)
- Andrea Ahles
- Institute of Pharmacology and Toxicology, Technical University of Munich (TUM), Munich, Germany
| | - Stefan Engelhardt
- Institute of Pharmacology and Toxicology, Technical University of Munich (TUM), Munich, Germany.
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany.
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18
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Nakajima S, Tsuchiya H, Ota M, Ogawa M, Yamada S, Yoshida R, Maeda J, Shirai H, Kasai T, Hirose J, Ninagawa K, Fujieda Y, Iwasaki T, Aizaki Y, Kajiyama H, Matsushita M, Kawakami E, Tamura N, Mimura T, Ohmura K, Morinobu A, Atsumi T, Tanaka Y, Takeuchi T, Tanaka S, Okamura T, Fujio K. Synovial Tissue Heterogeneity in Japanese Patients With Rheumatoid Arthritis Elucidated Using a Cell-Type Deconvolution Approach. Arthritis Rheumatol 2023; 75:2130-2136. [PMID: 37390361 DOI: 10.1002/art.42642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 04/26/2023] [Accepted: 06/27/2023] [Indexed: 07/02/2023]
Abstract
OBJECTIVE Recent advances in single-cell RNA sequencing technology have improved our understanding of the immunological landscape of rheumatoid arthritis (RA). We aimed to stratify the synovium from East Asian patients with RA by immune cell compositions and gain insight into the inflammatory drivers of each synovial phenotype. METHODS Synovial tissues were obtained from East Asian patients in Japan with RA (n = 41) undergoing articular surgery. The cellular composition was quantified by a deconvolution approach using a public single-cell-based reference. Inflammatory pathway activity was calculated by gene set variation analysis, and chromatin accessibility was evaluated using assay of transposase accessible chromatin-sequencing. RESULTS We stratified RA synovium into three distinct subtypes based on the hierarchical clustering of cellular composition data. One subtype was characterized by abundant HLA-DRAhigh synovial fibroblasts, autoimmune-associated B cells, GZMK+ GZMB+ CD8+ T cells, interleukin (IL)1-β+ monocytes, and plasmablasts. In addition, tumor necrosis factor (TNF)-α, interferons (IFNs), and IL-6 signaling were highly activated in this subtype, and the expression of various chemokines was significantly enhanced. Moreover, we found an open chromatin region overlapping with RA risk locus rs9405192 near the IRF4 gene, suggesting the genetic background influences the development of this inflammatory synovial state. The other two subtypes were characterized by increased IFNs and IL-6 signaling, and expression of molecules associated with degeneration, respectively. CONCLUSION This study adds insights into the synovial heterogeneity in East Asian patients and shows a promising link with predominant inflammatory signals. Evaluating the site of inflammation has the potential to lead to appropriate drug selection that matches the individual pathology.
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Affiliation(s)
- Sotaro Nakajima
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Haruka Tsuchiya
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Mineto Ota
- Department of Allergy and Rheumatology and Department of Functional Genomics and Immunological Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Megumi Ogawa
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Saeko Yamada
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Ryochi Yoshida
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Junko Maeda
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Harumi Shirai
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Taro Kasai
- Department of Orthopedic Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Jun Hirose
- Department of Orthopedic Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Keita Ninagawa
- Department of Rheumatology, Endocrinology and Nephrology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Yuichiro Fujieda
- Department of Rheumatology, Endocrinology and Nephrology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Takeshi Iwasaki
- Department of Rheumatology and Clinical Immunology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yoshimi Aizaki
- Department of Rheumatology and Applied Immunology, Faculty of Medicine, Saitama Medical University, Saitama, Japan
| | - Hiroshi Kajiyama
- Department of Rheumatology and Applied Immunology, Faculty of Medicine, Saitama Medical University, Saitama, Japan
| | - Masakazu Matsushita
- Department of Internal Medicine and Rheumatology, Juntendo University School of Medicine, Tokyo, Japan
| | - Eiryo Kawakami
- Department of Artificial Intelligence Medicine, Chiba University Graduate School of Medicine, Chiba, Japan and Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN, Yokohama, Japan
| | - Naoto Tamura
- Department of Internal Medicine and Rheumatology, Juntendo University School of Medicine, Tokyo, Japan
| | - Toshihide Mimura
- Department of Rheumatology and Applied Immunology, Faculty of Medicine, Saitama Medical University, Saitama, Japan
| | - Koichiro Ohmura
- Department of Rheumatology and Clinical Immunology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Akio Morinobu
- Department of Rheumatology and Clinical Immunology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Tatsuya Atsumi
- Department of Rheumatology, Endocrinology and Nephrology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Yoshiya Tanaka
- The First Department of Internal Medicine, School of Medicine, University of Occupational and Environmental Health, Fukuoka, Japan
| | - Tsutomu Takeuchi
- Division of Rheumatology, Department of Internal Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Sakae Tanaka
- Department of Orthopedic Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Tomohisa Okamura
- Department of Functional Genomics and Immunological Diseases, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Keishi Fujio
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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19
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Gao F, Pan R, Fan T, Liu L, Pan H. Identification of heel bone mineral density as a risk factor of Alzheimer's disease by analyzing large-scale genome-wide association studies datasets. Front Cell Dev Biol 2023; 11:1247067. [PMID: 38099291 PMCID: PMC10720361 DOI: 10.3389/fcell.2023.1247067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 11/06/2023] [Indexed: 12/17/2023] Open
Abstract
Introduction: Both low bone mineral density (BMD) and Alzheimer's disease (AD) commonly co_occur in the older adult. Until now, the association between AD and BMD has been widely reported by observational studies. However, Mendelian randomization (MR) studies did not support the causal association between BMD and AD. We think that the lack of significant causal association between AD and BMD identified by recent MR studies may be caused by small number of potential instrumental variables. Methods: We conduct a MR study to evaluate the causal effect of heel BMD on the risk of AD using 1,362 genome-wide significant and independent (p < 5.00E-08) heel BMD genetic variants as the potential instrumental variables, which are identified by a large-scale genome wide association study (GWAS) of heel BMD in 394,929 UK Biobank individuals. Using these 1,362 genome-wide significant and independent heel BMD genetic variants, we extracted their corresponding AD GWAS summary results in IGAP AD GWAS dataset (n = 63,926) and FinnGen AD GWAS dataset (n = 377,277). Five methods including inverse-variance weighted meta-analysis (IVW), weighted median, MR-Egger, MR-PRESSO, and MRlap were selected to perform the MR analysis. 951 of these 1,362 genetic variants are available in AD GWAS dataset. Results: We observed statistically significant causal effect of heel BMD on the risk of AD using IVW in IGAP AD GWAS dataset (OR = 1.048, 95%CI: 1.002-1.095, p = 0.04) and FinnGen AD GWAS dataset (OR = 1.053, 95% CI:1.011-1.098, p = 0.011). Importantly, meta-analysis of IVW estimates from IGAP and FinnGen further supported the causal effect of heel BMD on the risk of AD (OR = 1.051, 95% CI: 1.02-1.083, p = 0.0013). Discussion: Collectively, our current MR study supports heel BMD to be a risk factor of AD by analyzing the large-scale heel BMD and AD GWAS datasets. The potential mechanisms underlying the association between heel BMD and AD should be further evaluated in future.
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Affiliation(s)
- Feng Gao
- Department of Orthopedics, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Rongrong Pan
- Department of Orthopedics, Huangshan People’s Hospital, Huangshan, China
| | - Taixuan Fan
- Department of Orthopedics, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Lingling Liu
- Department of Orthopedics, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Haile Pan
- Department of Orthopedics, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
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20
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Ho-Le TP, Tran TS, Nguyen HG, Center JR, Eisman JA, Nguyen TV. Genetic Prediction of Lifetime Risk of Fracture. J Clin Endocrinol Metab 2023; 108:e1403-e1412. [PMID: 37165700 DOI: 10.1210/clinem/dgad254] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 04/15/2023] [Accepted: 05/09/2023] [Indexed: 05/12/2023]
Abstract
CONTEXT Fragility fracture is a significant public health problem because it is associated with increased mortality. We want to find out whether the risk of fracture can be predicted from the time of birth. OBJECTIVE To examine the association between a polygenic risk score (PRS) and lifetime fracture risk. METHODS This population-based prospective study involved 3515 community-dwelling individuals aged 60+ years who have been followed for up to 20 years. Femoral neck bone mineral density (BMD) was measured by dual-energy x-ray absorptiometry. A PRS was created by summing the weighted number of risk alleles for each single nucleotide polymorphism using BMD-associated coefficients. Fragility fractures were radiologically ascertained, whereas mortality was ascertained through a state registry. Residual lifetime risk of fracture (RLRF) was estimated by survival analysis. RESULTS The mortality-adjusted RLRF for women and men was 36% (95% CI, 34%-39%) and 21% (18%-24%), respectively. Individuals with PRS > 4.24 (median) had a greater risk (1.2-fold in women and 1.1-fold in men) than the population average risk. For hip fracture, the average RLRF was 10% (95% CI, 8%-12%) for women and ∼5% (3%-7%) for men; however, the risk was significantly increased by 1.5-fold and 1.3-fold for women and men with high PRS, respectively. CONCLUSION A genetic profiling of BMD-associated genetic variants is associated with the residual lifetime risk of fracture, suggesting the potential for incorporating the polygenic risk score in personalized fracture risk assessment.
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Affiliation(s)
- Thao P Ho-Le
- School of Biomedical Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Thach S Tran
- School of Biomedical Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia
- Skeletal Disease Group, Garvan Institute of Medical Research, Sydney, NSW 2010, Australia
| | - Huy G Nguyen
- School of Biomedical Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Jacqueline R Center
- Skeletal Disease Group, Garvan Institute of Medical Research, Sydney, NSW 2010, Australia
- School of Medicine Sydney, University of Notre Dame Australia, Sydney, NSW 2010, Australia
| | - John A Eisman
- Skeletal Disease Group, Garvan Institute of Medical Research, Sydney, NSW 2010, Australia
- School of Medicine Sydney, University of Notre Dame Australia, Sydney, NSW 2010, Australia
| | - Tuan V Nguyen
- School of Biomedical Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia
- School of Medicine Sydney, University of Notre Dame Australia, Sydney, NSW 2010, Australia
- School of Population Health, UNSW Medicine, UNSW, Sydney 2033, Australia
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21
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Kavousi M, Bos MM, Barnes HJ, Lino Cardenas CL, Wong D, Lu H, Hodonsky CJ, Landsmeer LPL, Turner AW, Kho M, Hasbani NR, de Vries PS, Bowden DW, Chopade S, Deelen J, Benavente ED, Guo X, Hofer E, Hwang SJ, Lutz SM, Lyytikäinen LP, Slenders L, Smith AV, Stanislawski MA, van Setten J, Wong Q, Yanek LR, Becker DM, Beekman M, Budoff MJ, Feitosa MF, Finan C, Hilliard AT, Kardia SLR, Kovacic JC, Kral BG, Langefeld CD, Launer LJ, Malik S, Hoesein FAAM, Mokry M, Schmidt R, Smith JA, Taylor KD, Terry JG, van der Grond J, van Meurs J, Vliegenthart R, Xu J, Young KA, Zilhão NR, Zweiker R, Assimes TL, Becker LC, Bos D, Carr JJ, Cupples LA, de Kleijn DPV, de Winther M, den Ruijter HM, Fornage M, Freedman BI, Gudnason V, Hingorani AD, Hokanson JE, Ikram MA, Išgum I, Jacobs DR, Kähönen M, Lange LA, Lehtimäki T, Pasterkamp G, Raitakari OT, Schmidt H, Slagboom PE, Uitterlinden AG, Vernooij MW, Bis JC, Franceschini N, Psaty BM, Post WS, Rotter JI, Björkegren JLM, O'Donnell CJ, Bielak LF, Peyser PA, Malhotra R, van der Laan SW, Miller CL. Multi-ancestry genome-wide study identifies effector genes and druggable pathways for coronary artery calcification. Nat Genet 2023; 55:1651-1664. [PMID: 37770635 PMCID: PMC10601987 DOI: 10.1038/s41588-023-01518-4] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 08/29/2023] [Indexed: 09/30/2023]
Abstract
Coronary artery calcification (CAC), a measure of subclinical atherosclerosis, predicts future symptomatic coronary artery disease (CAD). Identifying genetic risk factors for CAC may point to new therapeutic avenues for prevention. Currently, there are only four known risk loci for CAC identified from genome-wide association studies (GWAS) in the general population. Here we conducted the largest multi-ancestry GWAS meta-analysis of CAC to date, which comprised 26,909 individuals of European ancestry and 8,867 individuals of African ancestry. We identified 11 independent risk loci, of which eight were new for CAC and five had not been reported for CAD. These new CAC loci are related to bone mineralization, phosphate catabolism and hormone metabolic pathways. Several new loci harbor candidate causal genes supported by multiple lines of functional evidence and are regulators of smooth muscle cell-mediated calcification ex vivo and in vitro. Together, these findings help refine the genetic architecture of CAC and extend our understanding of the biological and potential druggable pathways underlying CAC.
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Affiliation(s)
- Maryam Kavousi
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
| | - Maxime M Bos
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Hanna J Barnes
- Cardiovascular Research Center, Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Christian L Lino Cardenas
- Cardiovascular Research Center, Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Doris Wong
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Haojie Lu
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Chani J Hodonsky
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Lennart P L Landsmeer
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Adam W Turner
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Minjung Kho
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Graduate School of Data Science, Seoul National University, Seoul, Republic of Korea
| | - Natalie R Hasbani
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Center at Houston, Houston, TX, USA
| | - Paul S de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Center at Houston, Houston, TX, USA
| | - Donald W Bowden
- Department of Biochemistry, Wake Forest University Health Sciences, Winston-Salem, NC, USA
| | - Sandesh Chopade
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- University College London British Heart Foundation Research Accelerator Centre, London, UK
| | - Joris Deelen
- Biomedical Data Sciences, Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
- Max Planck Institute for Biology of Aging, Cologne, Germany
| | - Ernest Diez Benavente
- Laboratory of Experimental Cardiology, Division of Heart and Lungs, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Edith Hofer
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University of Graz, Graz, Austria
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria
| | | | - Sharon M Lutz
- Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care, Boston, MA, USA
| | - Leo-Pekka Lyytikäinen
- Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Lotte Slenders
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Albert V Smith
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
- Icelandic Heart Association, Kopavogur, Iceland
| | - Maggie A Stanislawski
- Department of Biomedical Informatics, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA
| | - Jessica van Setten
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands
| | - Quenna Wong
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Lisa R Yanek
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Diane M Becker
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Marian Beekman
- Biomedical Data Sciences, Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Matthew J Budoff
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Mary F Feitosa
- Department of Genetics, Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA
| | - Chris Finan
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- University College London British Heart Foundation Research Accelerator Centre, London, UK
- Department of Cardiology, Division of Heart and Lungs, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands
| | | | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Jason C Kovacic
- Victor Chang Cardiac Research Institute, Darlinghurst, New South Wales, Australia
- St Vincent's Clinical School, University of NSW, Sydney, New South Wales, Australia
- The Zena and Michael A. Wiener Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
| | - Brian G Kral
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Carl D Langefeld
- Department of Biostatistical Sciences and Data Science, Wake Forest University Health Sciences, Winston-Salem, NC, USA
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Shaista Malik
- Susan Samueli Integrative Health Institute, Department of Medicine, University of California Irvine, Irvine, CA, USA
| | | | - Michal Mokry
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Laboratory of Experimental Cardiology, Division of Heart and Lungs, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands
| | - Reinhold Schmidt
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University of Graz, Graz, Austria
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - James G Terry
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jeroen van der Grond
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Joyce van Meurs
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Rozemarijn Vliegenthart
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Jianzhao Xu
- Department of Biochemistry, Wake Forest University Health Sciences, Winston-Salem, NC, USA
| | - Kendra A Young
- Department of Epidemiology, University of Colorado, Anschutz Medical Campus, Denver, CO, USA
| | | | - Robert Zweiker
- Department of Internal Medicine, Division of Cardiology, Medical University of Graz, Graz, Austria
| | - Themistocles L Assimes
- VA Palo Alto Healthcare System, Palo Alto, CA, USA
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Lewis C Becker
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Daniel Bos
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - J Jeffrey Carr
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - L Adrienne Cupples
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - Dominique P V de Kleijn
- Department of Vascular Surgery, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands
| | - Menno de Winther
- Department of Medical Biochemistry, Experimental Vascular Biology, Amsterdam Cardiovascular Sciences: Atherosclerosis and Ischemic syndromes, Amsterdam Infection and Immunity: Inflammatory diseases, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Hester M den Ruijter
- Laboratory of Experimental Cardiology, Division of Heart and Lungs, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands
| | - Myriam Fornage
- Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Barry I Freedman
- Department of Internal Medicine, Wake Forest University Health Sciences, Winston-Salem, NC, USA
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, School of Public Health, University of Iceland, Reykjavik, Iceland
| | - Aroon D Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, London, UK
- University College London British Heart Foundation Research Accelerator Centre, London, UK
| | - John E Hokanson
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Ivana Išgum
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - David R Jacobs
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Mika Kähönen
- Department of Clinical Physiology, Tampere University Hospital and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Leslie A Lange
- Department of Biomedical Informatics, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA
| | - Terho Lehtimäki
- Department of Clinical Chemistry, Fimlab Laboratories and Finnish Cardiovascular Research Center-Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Gerard Pasterkamp
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Olli T Raitakari
- Centre for Population Health Research, University of Turku and Turku University Hospital, Turku, Finland
- Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
- Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Helena Schmidt
- Gottfried Schatz Research Center (for Cell Signaling, Metabolism and Aging), Medical University of Graz, Graz, Austria
| | - P Eline Slagboom
- Biomedical Data Sciences, Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - André G Uitterlinden
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Vascular Surgery, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Nora Franceschini
- Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Departments of Epidemiology, and Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Wendy S Post
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation (formerly Los Angeles Biomedical Research Institute) at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Johan L M Björkegren
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York City, NY, USA
- Department of Medicine, Integrated Cardio Metabolic Centre, Karolinska Institutet, Huddinge, Sweden
| | - Christopher J O'Donnell
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Cardiology Section, Department of Medicine, Veterans Affairs Boston Healthcare System, Boston, MA, USA
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Rajeev Malhotra
- Cardiovascular Research Center, Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Sander W van der Laan
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Clint L Miller
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA.
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA.
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22
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Cheng Y, Justice A, Wang Z, Li B, Hancock DB, Johnson EO, Xu K. Cis-meQTL for cocaine use-associated DNA methylation in an HIV-positive cohort show pleiotropic effects on multiple traits. BMC Genomics 2023; 24:556. [PMID: 37730558 PMCID: PMC10510240 DOI: 10.1186/s12864-023-09661-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 09/08/2023] [Indexed: 09/22/2023] Open
Abstract
BACKGROUND Cocaine use (CU) is associated with psychiatric and medical diseases. Little is known about the mechanisms of CU-related comorbidities. Findings from preclinical and clinical studies have suggested that CU is associated with aberrant DNA methylation (DNAm) that may be influenced by genetic variants [i.e., methylation quantitative trait loci (meQTLs)]. In this study, we mapped cis-meQTLs for CU-associated DNAm sites (CpGs) in an HIV-positive cohort (Ntotal = 811) and extended the meQTLs to multiple traits. RESULTS We conducted cis-meQTL analysis for 224 candidate CpGs selected for their association with CU in blood. We identified 7,101 significant meQTLs [false discovery rate (FDR) < 0.05], which mostly mapped to genes involved in immunological functions and were enriched in immune pathways. We followed up the meQTLs using phenome-wide association study and trait enrichment analyses, which revealed 9 significant traits. We tested for causal effects of CU on these 9 traits using Mendelian Randomization and found evidence that CU plays a causal role in increasing hypertension (p-value = 2.35E-08) and decreasing heel bone mineral density (p-value = 1.92E-19). CONCLUSIONS These findings suggest that genetic variants for CU-associated DNAm have pleiotropic effects on other relevant traits and provide new insights into the causal relationships between cocaine use and these complex traits.
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Affiliation(s)
- Youshu Cheng
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, 06511, USA
- VA Connecticut Healthcare System, West Haven, CT, 06516, USA
| | - Amy Justice
- VA Connecticut Healthcare System, West Haven, CT, 06516, USA
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, 06511, USA
| | - Zuoheng Wang
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, 06511, USA
| | - Boyang Li
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, 06511, USA
- VA Connecticut Healthcare System, West Haven, CT, 06516, USA
| | - Dana B Hancock
- GenOmics, Bioinformatics, and Translational Research Center, RTI International, Research Triangle Park, NC, USA
| | - Eric O Johnson
- GenOmics, Bioinformatics, and Translational Research Center, RTI International, Research Triangle Park, NC, USA
- Fellow Program, RTI International, Research Triangle Park, NC, USA
| | - Ke Xu
- VA Connecticut Healthcare System, West Haven, CT, 06516, USA.
- Department of Psychiatry, Yale School of Medicine, 300 George Street, New Haven, CT, 06511, USA.
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23
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Collins M, September AV. Are commercial genetic injury tests premature? Scand J Med Sci Sports 2023; 33:1584-1597. [PMID: 37243491 DOI: 10.1111/sms.14406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 05/09/2023] [Accepted: 05/16/2023] [Indexed: 05/28/2023]
Abstract
INTRODUCTION Several direct-to-consumer (DTC) genetic testing companies have emerged that claim to be able to test for susceptibility for musculoskeletal injuries. Although there are several publications on the emergence of this industry, none have critically evaluated the evidence for the use of genetic polymorphisms in commercial tests. The aim of this review was to identify, where possible, the polymorphisms and to evaluate the current scientific evidence for their inclusion. RESULTS The most common polymorphisms included COL1A1 rs1800012, COL5A1 rs12722, and GDF5 rs143383. The current evidence suggests that it is premature or even not viable to include these three polymorphisms as markers of injury risk. A unique set of injury-specific polymorphisms, which do not include COL1A1, COL5A1, or GDF5, identified from genome-wide association studies (GWAS) is used by one company in their tests for 13 sports injuries. However, of the 39 reviewed polymorphisms, 22 effective alleles are rare and absent in African, American, and/or Asian populations. Even when informative in all populations, the sensitivity of many of the genetic markers was low and/or has not been independently validated in follow-up studies. CONCLUSIONS The current evidence suggests it is premature to include any of the reviewed polymorphisms identified by GWAS or candidate gene approaches in commercial genetic tests. The association of MMP7 rs1937810 with Achilles tendon injuries, and SAP30BP rs820218 and GLCCI1 rs4725069 with rotator cuff injuries does warrant further investigation. Based on current evidence, it remains premature to market any commercial genetic test to determine susceptibility to musculoskeletal injuries.
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Affiliation(s)
- Malcolm Collins
- Health through Physical Activity, Lifestyle and Sport Research Centre (HPALS), Division of Physiological Sciences, Department of Human Biology, University of Cape Town, Cape Town, South Africa
- International Federation of Sports Medicine (FIMS) Collaborative Centre of Sports Medicine, Cape Town, South Africa
| | - Alison V September
- Health through Physical Activity, Lifestyle and Sport Research Centre (HPALS), Division of Physiological Sciences, Department of Human Biology, University of Cape Town, Cape Town, South Africa
- International Federation of Sports Medicine (FIMS) Collaborative Centre of Sports Medicine, Cape Town, South Africa
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24
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Yan M, Tsukasaki M, Muro R, Ando Y, Nakamura K, Komatsu N, Nitta T, Okamura T, Okamoto K, Takayanagi H. Identification of an intronic enhancer regulating RANKL expression in osteocytic cells. Bone Res 2023; 11:43. [PMID: 37563119 PMCID: PMC10415388 DOI: 10.1038/s41413-023-00277-6] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 06/22/2023] [Accepted: 07/05/2023] [Indexed: 08/12/2023] Open
Abstract
The bony skeleton is continuously renewed throughout adult life by the bone remodeling process, in which old or damaged bone is removed by osteoclasts via largely unknown mechanisms. Osteocytes regulate bone remodeling by producing the osteoclast differentiation factor RANKL (encoded by the TNFSF11 gene). However, the precise mechanisms underlying RANKL expression in osteocytes are still elusive. Here, we explored the epigenomic landscape of osteocytic cells and identified a hitherto-undescribed osteocytic cell-specific intronic enhancer in the TNFSF11 gene locus. Bioinformatics analyses showed that transcription factors involved in cell death and senescence act on this intronic enhancer region. Single-cell transcriptomic data analysis demonstrated that cell death signaling increased RANKL expression in osteocytic cells. Genetic deletion of the intronic enhancer led to a high-bone-mass phenotype with decreased levels of RANKL in osteocytic cells and osteoclastogenesis in the adult stage, while RANKL expression was not affected in osteoblasts or lymphocytes. These data suggest that osteocytes may utilize a specialized regulatory element to facilitate osteoclast formation at the bone surface to be resorbed by linking signals from cellular senescence/death and RANKL expression.
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Affiliation(s)
- Minglu Yan
- Department of Immunology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Masayuki Tsukasaki
- Department of Osteoimmunology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, Tokyo, Japan.
| | - Ryunosuke Muro
- Department of Immunology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Yutaro Ando
- Department of Immunology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Microbiology, Tokyo Dental College, Tokyo, Japan
| | - Kazutaka Nakamura
- Department of Immunology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, Tokyo, Japan
- Department of Oral and Maxillofacial Surgery, Department of Sensory and Motor System Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Noriko Komatsu
- Department of Immunology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Takeshi Nitta
- Department of Immunology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Tadashi Okamura
- Department of Laboratory Animal Medicine, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Kazuo Okamoto
- Department of Osteoimmunology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Hiroshi Takayanagi
- Department of Immunology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, Tokyo, Japan.
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25
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Li Y, Xiong Z, Zhang M, Hysi PG, Qian Y, Adhikari K, Weng J, Wu S, Du S, Gonzalez-Jose R, Schuler-Faccini L, Bortolini MC, Acuna-Alonzo V, Canizales-Quinteros S, Gallo C, Poletti G, Bedoya G, Rothhammer F, Wang J, Tan J, Yuan Z, Jin L, Uitterlinden AG, Ghanbari M, Ikram MA, Nijsten T, Zhu X, Lei Z, Jia P, Ruiz-Linares A, Spector TD, Wang S, Kayser M, Liu F. Combined genome-wide association study of 136 quantitative ear morphology traits in multiple populations reveal 8 novel loci. PLoS Genet 2023; 19:e1010786. [PMID: 37459304 PMCID: PMC10351707 DOI: 10.1371/journal.pgen.1010786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 05/16/2023] [Indexed: 07/20/2023] Open
Abstract
Human ear morphology, a complex anatomical structure represented by a multidimensional set of correlated and heritable phenotypes, has a poorly understood genetic architecture. In this study, we quantitatively assessed 136 ear morphology traits using deep learning analysis of digital face images in 14,921 individuals from five different cohorts in Europe, Asia, and Latin America. Through GWAS meta-analysis and C-GWASs, a recently introduced method to effectively combine GWASs of many traits, we identified 16 genetic loci involved in various ear phenotypes, eight of which have not been previously associated with human ear features. Our findings suggest that ear morphology shares genetic determinants with other surface ectoderm-derived traits such as facial variation, mono eyebrow, and male pattern baldness. Our results enhance the genetic understanding of human ear morphology and shed light on the shared genetic contributors of different surface ectoderm-derived phenotypes. Additionally, gene editing experiments in mice have demonstrated that knocking out the newly ear-associated gene (Intu) and a previously ear-associated gene (Tbx15) causes deviating mouse ear morphology.
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Affiliation(s)
- Yi Li
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, China
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, China
| | - Ziyi Xiong
- Department of Genetic Identification, Erasmus MC, University Medical Center, the Netherlands
- Department of Epidemiology, Erasmus MC, University Medical Center, the Netherlands
| | - Manfei Zhang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, China
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University, China
| | - Pirro G. Hysi
- Department of Twin Research and Genetic Epidemiology, King’s College London, United Kingdom
| | - Yu Qian
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, China
- Beijing No.8 High School, Beijing, China
| | - Kaustubh Adhikari
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, United Kingdom
- School of Mathematics and Statistics, Faculty of Science, Technology, Engineering and Mathematics, The Open University, United Kingdom
| | - Jun Weng
- Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China
| | - Sijie Wu
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, China
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University, China
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, China
| | - Siyuan Du
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, China
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Ministry of Education, Shanghai Jiao Tong University, Shanghai, China
- University of Chinese Academy of Sciences, China
| | - Rolando Gonzalez-Jose
- Instituto Patagonico de Ciencias Sociales y Humanas, Centro Nacional Patagonico, CONICET, Argentina
| | | | | | - Victor Acuna-Alonzo
- Molecular Genetics Laboratory, National School of Anthropology and History, Mexico
| | - Samuel Canizales-Quinteros
- Unidad de Genomica de Poblaciones Aplicada a la Salud, Facultad de Quimica, UNAM-Instituto Nacional de Medicina Genomica, Mexico
| | - Carla Gallo
- Laboratorios de Investigacion y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Peru
| | - Giovanni Poletti
- Laboratorios de Investigacion y Desarrollo, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Peru
| | - Gabriel Bedoya
- GENMOL (Genetica Molecular), Universidad de Antioquia, Medellin, Colombia
| | | | - Jiucun Wang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University, China
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, China
| | - Jingze Tan
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, China
| | - Ziyu Yuan
- Fudan-Taizhou Institute of Health Sciences, China
| | - Li Jin
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, China
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, Zhangjiang Fudan International Innovation Center, Fudan University, China
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, China
- Fudan-Taizhou Institute of Health Sciences, China
| | - André G. Uitterlinden
- Department of Epidemiology, Erasmus MC, University Medical Center, the Netherlands
- Department of Internal Medicine, Erasmus MC, University Medical Center, the Netherlands
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC, University Medical Center, the Netherlands
| | - M. Arfan Ikram
- Department of Epidemiology, Erasmus MC, University Medical Center, the Netherlands
| | - Tamar Nijsten
- Department of Dermatology, Erasmus MC, University Medical Center, the Netherlands
| | - Xiangyu Zhu
- Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, China
| | - Zhen Lei
- Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, China
| | - Peilin Jia
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, China
| | - Andres Ruiz-Linares
- Department of Genetics, Evolution and Environment, and UCL Genetics Institute, University College London, United Kingdom
- Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, China
- Aix-Marseille Universite, CNRS, EFS, ADES, France
| | - Timothy D. Spector
- Department of Twin Research and Genetic Epidemiology, King’s College London, United Kingdom
| | - Sijia Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, China
- Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, China
| | - Manfred Kayser
- Department of Genetic Identification, Erasmus MC, University Medical Center, the Netherlands
| | - Fan Liu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, China
- Department of Genetic Identification, Erasmus MC, University Medical Center, the Netherlands
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26
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Wang C, Tian Z, Wen D, Qu W, Xu R, Liu Y, Jia H, Tang X, Li J, Zha L, Liu Y. Preliminary study on genetic factors related to Demirjian's tooth age estimation method based on genome-wide association analysis. Int J Legal Med 2023:10.1007/s00414-023-03008-y. [PMID: 37133749 DOI: 10.1007/s00414-023-03008-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 04/24/2023] [Indexed: 05/04/2023]
Abstract
The age determination of individuals, especially minors, is critical in forensic research. In forensic practice, dental age estimation is one of the most commonly used methods for determining age as teeth are easy to preserve and relatively resistant to environmental factors. Tooth development is affected and regulated by genetic factors; however, these are not incorporated into current commonly used tooth age inference methods, leading to unreliable results. Here, we established a Demirjian and a Cameriere tooth age estimation-based methods suitable for use in children in southern China. By using the difference between the inferred age and the actual age (MD) as the phenotype, we identified 65 and 49 SNPs related to tooth age estimation from 743,722 loci among 171 children in southern China through a genome-wide association analysis (p<0.0001). We also conducted a genome-wide association study on dental development stage (DD) using the Demirjian tooth age estimation method and screened two sets of SNP sites (52 and 26) based on whether age difference was considered. The gene function enrichment analysis of these SNPs found that they were related to bone development and mineralization. Although SNP sites screened based on MD seem to improve the accuracy of tooth age estimation, there is little correlation between these SNPs and an individual's Demirjian morphological stage. In conclusion, we found that individual genotypes can affect tooth age estimation, and based on different phenotypic analysis models, we have identified some novel SNP sites related to tooth age inference and Demirjian's tooth development stage. These studies provide a reference for subsequent phenotypic selection based on tooth age inference analysis, and the results could possibly be used in the future to make forensic age estimation more accurate.
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Affiliation(s)
- Chudong Wang
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, No. 172 Tongzipo Road, Changsha, Hunan Province, 410013, People's Republic of China
| | - ZhiKai Tian
- Department of Oral Implantology, Xiangya Hospital of Stomatology, Central South University, No. 72 Xiangya Road, Kaifu District, Changsha, Hunan Province, People's Republic of China
| | - Dan Wen
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, No. 172 Tongzipo Road, Changsha, Hunan Province, 410013, People's Republic of China
| | - Weifeng Qu
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, No. 172 Tongzipo Road, Changsha, Hunan Province, 410013, People's Republic of China
| | - Ruyi Xu
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, No. 172 Tongzipo Road, Changsha, Hunan Province, 410013, People's Republic of China
| | - Yi Liu
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, No. 172 Tongzipo Road, Changsha, Hunan Province, 410013, People's Republic of China
| | - Hongtao Jia
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, No. 172 Tongzipo Road, Changsha, Hunan Province, 410013, People's Republic of China
| | - Xuan Tang
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, No. 172 Tongzipo Road, Changsha, Hunan Province, 410013, People's Republic of China
| | - Jienan Li
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, No. 172 Tongzipo Road, Changsha, Hunan Province, 410013, People's Republic of China
| | - Lagabaiyila Zha
- Department of Forensic Science, School of Basic Medical Sciences, Central South University, No. 172 Tongzipo Road, Changsha, Hunan Province, 410013, People's Republic of China.
| | - Ying Liu
- Department of Oral Implantology, Xiangya Hospital of Stomatology, Central South University, No. 72 Xiangya Road, Kaifu District, Changsha, Hunan Province, People's Republic of China.
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Xiao X, Wu Q. The clinical utility of the BMD-related comprehensive genome-wide polygenic score in identifying individuals with a high risk of osteoporotic fractures. Osteoporos Int 2023; 34:681-692. [PMID: 36622390 PMCID: PMC11225087 DOI: 10.1007/s00198-022-06654-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 12/20/2022] [Indexed: 01/10/2023]
Abstract
The potential of bone mineral density (BMD)-related genome-wide polygenic score (PGS) in identifying individuals with a high risk of fractures remains unclear. This study suggests that an efficient PGS enables the identification of strata with up to a 1.5-fold difference in fracture incidence. Incorporating PGS into clinical diagnosis is anticipated to increase the population-level screening benefits. PURPOSE This study sought to construct genome-wide polygenic scores for femoral neck and total body BMD and to estimate their potential in identifying individuals with a high risk of osteoporotic fractures. METHODS Genome-wide polygenic scores were developed and validated for femoral neck and total body BMD. We externally tested the PGSs, both by themselves and in combination with available clinical risk factors, in 455,663 European ancestry individuals from the UK Biobank. The predictive accuracy of the developed genome-wide PGS was also compared with previously published restricted PGS employed in fracture risk assessment. RESULTS For each unit decrease in PGSs, the genome-wide PGSs were associated with up to 1.17-fold increased fracture risk. Out of four studied PGSs, [Formula: see text] (HR: 1.03; 95%CI 1.01-1.05, p = 0.001) had the weakest and the [Formula: see text] (HR: 1.17; 95%CI 1.15-1.19, p < 0.0001) had the strongest association with an incident fracture. In the reclassification analysis, compared to the FRAX base model, the models with [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] improved the reclassification of fracture by 1.2% (95% CI, 1.0 to 1.3%), 0.2% (95% CI, 0.1 to 0.3%), 1.4% (95% CI, 1.3 to 1.5%), and 2.2% (95% CI, 2.1 to 2.4%), respectively. CONCLUSIONS Our findings suggested that an efficient PGS estimate enables the identification of strata with up to a 1.7-fold difference in fracture incidence. Incorporating PGS information into clinical diagnosis is anticipated to increase the benefits of screening programs at the population level.
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Affiliation(s)
- Xiangxue Xiao
- Nevada Institute of Personalized Medicine, College of Science, University of Nevada, Las Vegas, NV, USA
- Department of Epidemiology and Biostatistics, School of Public Health, University of Nevada Las Vegas, Las Vegas, NV, USA
| | - Qing Wu
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA.
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Hu J, Li Y, Wang Z, Li X, Hou T, Ning Z, Huang R, Ma C, Yuan X, Wang D. Association of plant-based dietary patterns with the risk of osteoporosis in community-dwelling adults over 60 years: a cross-sectional study. Osteoporos Int 2023; 34:915-923. [PMID: 36856795 DOI: 10.1007/s00198-023-06700-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Accepted: 02/08/2023] [Indexed: 03/02/2023]
Abstract
UNLABELLED Plant foods are rich in many important micronutrients that are beneficial for bone health. This cross-sectional study of 9613 community-dwelling older adults found that more consumption of healthy plant foods and less consumption of animal foods and unhealthy plant foods were associated with a lower risk of osteoporosis. INTRODUCTION Osteoporosis,a common chronic disease in older adults, threatens their health. Many nutrients in plant foods are important for preventing osteoporosis. However, the relationship between plant-based dietary patterns and osteoporosis remains unclear. This study aimed to investigate the cross-sectional association between plant-based dietary patterns and osteoporosis in older adults. METHODS This study was conducted among 9613 community-dwelling older adults in Liaoning Province, China. The effective food frequency questionnaire (FFQ) and plant-based diet index (PDI) were used to evaluate compliance with plant-based dietary patterns. Osteoporosis was defined based on heel ultrasound. We analyzed the association between healthy plant-based diet index (HPDI) and unhealthful plant-based diet index (UPDI) and the risk of osteoporosis. RESULTS A higher PDI was associated with higher bone mineral density (BMD) in older adults. In logistic regression models, the highest quartile of PDI and HPDI had a significantly lower risk of osteoporosis than the lowest quartile, whereas UPDI in the highest quartile was associated with a higher risk of osteoporosis. There was a dose-response relationship between the three indices and the risk of osteoporosis. Subgroup analysis revealed differences in the relationship between HPDI and the risk of osteoporosis according to gender and age. CONCLUSIONS Older adults, especially women, consume more healthy plant foods and reduce the consumption of animal foods and unhealthy plant foods, which was associated with a lower risk of osteoporosis.
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Affiliation(s)
- Junwei Hu
- Department of Gerontology and Geriatrics, Shengjing Hospital of China Medical University, Shenyang, China
- Department of Health Statistics, School of Public Health, China Medical University, Shenyang, 110122, Liaoning, China
| | - Yi Li
- Department of Gerontology and Geriatrics, Shengjing Hospital of China Medical University, Shenyang, China
- Department of Health Statistics, School of Public Health, China Medical University, Shenyang, 110122, Liaoning, China
| | - Zheng Wang
- Department of Health Statistics, School of Public Health, China Medical University, Shenyang, 110122, Liaoning, China
| | - Xin Li
- Department of Health Statistics, School of Public Health, China Medical University, Shenyang, 110122, Liaoning, China
| | - Tianbo Hou
- Department of Health Statistics, School of Public Health, China Medical University, Shenyang, 110122, Liaoning, China
| | - Zibo Ning
- Department of Health Statistics, School of Public Health, China Medical University, Shenyang, 110122, Liaoning, China
| | - Runnian Huang
- Department of Health Statistics, School of Public Health, China Medical University, Shenyang, 110122, Liaoning, China
| | - Chunhua Ma
- Department of Health Statistics, School of Public Health, China Medical University, Shenyang, 110122, Liaoning, China
| | - Xiaoyue Yuan
- Department of Health Statistics, School of Public Health, China Medical University, Shenyang, 110122, Liaoning, China
| | - Difei Wang
- Department of Gerontology and Geriatrics, Shengjing Hospital of China Medical University, Shenyang, China.
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He D, Pan C, Zhao Y, Wei W, Qin X, Cai Q, Shi S, Chu X, Zhang N, Jia Y, Wen Y, Cheng B, Liu H, Feng R, Zhang F, Xu P. Exome-wide screening identifies novel rare risk variants for bone mineral density. Osteoporos Int 2023; 34:965-975. [PMID: 36849660 DOI: 10.1007/s00198-023-06710-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Accepted: 02/14/2023] [Indexed: 03/01/2023]
Abstract
UNLABELLED Bone mineral density (BMD) is an independent risk factor of osteoporosis-related fractures. We performed gene-based burden tests to assess the association between rare variants and BMD, and identified several BMD candidate genes. PURPOSE BMD is highly heritable and a major predictor of osteoporotic fractures, but its genetic basis remains unclear. We aimed to identify rare risk variants contributing to BMD. METHODS Utilizing the newly released UK Biobank 200,643 exome dataset, we conducted a gene-based exome-wide association study in males and females, respectively. First, 100,639 males and 117,338 females with BMD values were included in the polygenic risk scores (PRS) analysis. Among individuals with lower 30% PRS, cases were individuals with top 10% BMD, and individuals with bottom 10% BMD were the controls. Considering the effects of vitamin D (VD), individuals with the highest 30% VD concentration were selected for VD-BMD analysis. After quality control, 741 males and 697 females were included in the BMD analysis, and 717 males and 708 females were included in the VD-BMD analysis. The variants were annotated by ANNOVAR software, then BMD and VD-BMD qualified variants were imported into the SKAT R-package to perform gene-based burden tests, respectively. RESULTS The gene-based burden test of the exonic variants identified genome-wide candidate associations in ANKRD18A (P = 1.60 × 10-5, PBonferroni adjust = 2.11 × 10-3), C22orf31 (P = 3.49 × 10-4, PBonferroni adjust = 3.17 × 10-2), and SPATC1L (P = 1.09 × 10-5, PBonferroni adjust = 8.80 × 10-3). For VD-BMD analysis, three genes were associated with BMD, such as NIPAL1 (P = 1.06 × 10-3, PBonferroni adjust = 3.91 × 10-2). CONCLUSIONS Our study suggested that rare variants contribute to BMD, providing new sights for broadening the genetic structure of BMD.
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Affiliation(s)
- D He
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - C Pan
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Y Zhao
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - W Wei
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - X Qin
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Q Cai
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - S Shi
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - X Chu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - N Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Y Jia
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - Y Wen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - B Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - H Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China
| | - R Feng
- Department of Joint Surgery, HongHui Hospital, Xi'an Jiaotong University, Xi'an, China
| | - F Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, Xi'an Jiaotong University, Xi'an, China.
- Key Laboratory of Environment and Genes Related to Diseases of Ministry of Education of China, Xi'an Jiaotong University, Xi'an, China.
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an Jiaotong University, Xi'an, China.
- School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China.
| | - P Xu
- Department of Joint Surgery, HongHui Hospital, Xi'an Jiaotong University, Xi'an, China.
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Wu Q, Jung J. Genome-wide polygenic risk score for major osteoporotic fractures in postmenopausal women using associated single nucleotide polymorphisms. J Transl Med 2023; 21:127. [PMID: 36797788 PMCID: PMC9933300 DOI: 10.1186/s12967-023-03974-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 02/07/2023] [Indexed: 02/18/2023] Open
Abstract
BACKGROUND Osteoporosis is highly polygenic and heritable, with heritability ranging from 50 to 80%; most inherited susceptibility is associated with the cumulative effect of many common genetic variants. However, existing genetic risk scores (GRS) only provide a few percent predictive power for osteoporotic fracture. METHODS We derived and validated a novel genome-wide polygenic score (GPS) comprised of 103,155 common genetic variants to quantify this susceptibility and tested this GPS prediction ability in an independent dataset (n = 15,776). RESULTS Among postmenopausal women, we found a fivefold gradient in the risk of major osteoporotic fracture (MOF) (p < 0.001) and a 15.25-fold increased risk of severe osteoporosis (p < 0.001) across the GPS deciles. Compared with the remainder of the GPS distribution, the top GPS decile was associated with a 3.59-, 2.48-, 1.92-, and 1.58-fold increased risk of any fracture, MOF, hip fracture, and spine fracture, respectively. The top GPS decile also identified nearly twofold more high-risk osteoporotic patients than the top decile of conventional GRS based on 1103 conditionally independent genome-wide significant SNPs. Although the relative risk of severe osteoporosis for postmenopausal women at around 50 is relatively similar, the cumulative incident at 20-year follow-up is significantly different between the top GPS decile (13.7%) and the bottom decile (< 1%). In the subgroup analysis, the GPS transferability in non-Hispanic White is better than in other racial/ethnic groups. CONCLUSIONS This new method to quantify inherited susceptibility to osteoporosis and osteoporotic fracture affords new opportunities for clinical prevention and risk assessment.
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Affiliation(s)
- Qing Wu
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, 250 Lincoln Tower, 1800 Cannon Drive, Columbus, OH, 43210, USA.
| | - Jongyun Jung
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, 250 Lincoln Tower, 1800 Cannon Drive, Columbus, OH, 43210, USA
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Hillis DA, Garland T. Multiple solutions at the genomic level in response to selective breeding for high locomotor activity. Genetics 2023; 223:iyac165. [PMID: 36305689 PMCID: PMC9836024 DOI: 10.1093/genetics/iyac165] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 10/14/2022] [Indexed: 01/19/2023] Open
Abstract
Replicate lines under uniform selection often evolve in different ways. Previously, analyses using whole-genome sequence data for individual mice (Mus musculus) from 4 replicate High Runner lines and 4 nonselected control lines demonstrated genomic regions that have responded consistently to selection for voluntary wheel-running behavior. Here, we ask whether the High Runner lines have evolved differently from each other, even though they reached selection limits at similar levels. We focus on 1 High Runner line (HR3) that became fixed for a mutation at a gene of major effect (Myh4Minimsc) that, in the homozygous condition, causes a 50% reduction in hindlimb muscle mass and many pleiotropic effects. We excluded HR3 from SNP analyses and identified 19 regions not consistently identified in analyses with all 4 lines. Repeating analyses while dropping each of the other High Runner lines identified 12, 8, and 6 such regions. (Of these 45 regions, 37 were unique.) These results suggest that each High Runner line indeed responded to selection somewhat uniquely, but also that HR3 is the most distinct. We then applied 2 additional analytical approaches when dropping HR3 only (based on haplotypes and nonstatistical tests involving fixation patterns). All 3 approaches identified 7 new regions (as compared with analyses using all 4 High Runner lines) that include genes associated with activity levels, dopamine signaling, hippocampus morphology, heart size, and body size, all of which differ between High Runner and control lines. Our results illustrate how multiple solutions and "private" alleles can obscure general signatures of selection involving "public" alleles.
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Affiliation(s)
- David A Hillis
- Genetics, Genomics, and Bioinformatics Graduate Program, University of California, Riverside, CA 92521, USA
| | - Theodore Garland
- Department of Evolution, Ecology, and Organismal Biology, University of California, Riverside, CA 92521, USA
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Tuysuz B, Uludag Alkaya D, Geyik F, Alaylıoğlu M, Kasap B, Kurugoğlu S, Akman YE, Vural M, Bilguvar K. Biallelic frameshift variants in PHLDB1cause mild-type osteogenesis imperfecta with regressive spondylometaphyseal changes. J Med Genet 2022:jmg-2022-108763. [PMID: 36543534 DOI: 10.1136/jmg-2022-108763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 12/10/2022] [Indexed: 12/24/2022]
Abstract
BackgroundOsteogenesis imperfecta (OI) is a heterogeneous group of inherited disorders characterised by susceptibility to fractures, primarily due to defects in type 1 collagen. The aim of this study is to present a novel OI phenotype and its causative candidate gene.MethodsWhole-exome sequencing and clinical evaluation were performed in five patients from two unrelated families.PHLDB1mRNA expression in blood and fibroblasts was investigated by real-time PCR, and western blot analysis was further performed on skin fibroblasts.ResultsThe common findings among the five affected children were recurrent fractures and/or osteopaenia, platyspondyly, short and bowed long bones, and widened metaphyses. Metaphyseal and vertebral changes regressed after early childhood, and no fractures occurred under bisphosphonate treatment. We identified biallelic NM_001144758.3:c.2392dup and NM_001144758.3:c.2690_2693del pathogenic variants inPHLDB1in the affected patients, respectively, in the families; parents were heterozygous for these variants.PHLDB1encodes pleckstrin homology-like domain family B member-1 (PHLDB1) protein, which has a role in insulin-dependent Akt phosphorylation. Compared with controls, a decrease in the expression levels ofPHLDB1in the blood and skin fibroblast samples was detected. Western blot analysis of cultured fibroblasts further confirmed the loss of PHLDB1.ConclusionTwo biallelic frameshift variants in the candidate genePHLDB1were identified in independent families with a novel, mild-type, autosomal recessive OI. The demonstration of decreasedPHLDB1mRNA expression levels in blood and fibroblast samples supports the hypothesis thatPHLDB1pathogenic variants are causative for the observed phenotype.
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Xu C, Weng Z, Liu Q, Xu J, Liang J, Li W, Hu J, Huang T, Zhou Y, Gu A. Association of air pollutants and osteoporosis risk: The modifying effect of genetic predisposition. ENVIRONMENT INTERNATIONAL 2022; 170:107562. [PMID: 36228550 DOI: 10.1016/j.envint.2022.107562] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 09/20/2022] [Accepted: 10/02/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Limited studies have examined the association between air pollutants and osteoporosis incidence; however, the results are conflicting. We aimed to quantify the effects of selected air pollutants on osteoporosis risk and explore the modifying effect of genetic predisposition. METHODS A total of 422,955 subjects who did not have osteoporosis at baseline in the UK Biobank were included from 2006 to 2010. We conducted a Cox proportional hazards model with adjustment for covariates to examine the association between air pollutant scores and individual air pollutants and incident osteoporosis. Furthermore, a polygenic risk score (PRS) of osteoporosis was built and examined to determine whether genetic susceptibility modified the effect of air pollutants on osteoporosis. The relationship between air pollutants and osteoporosis was examined by using a restricted cubic spline (RCS) method. RESULTS After confounder adjustment, the results showed a remarkable increase in the risk of osteoporosis with each 10 unit increase in exposure to air pollution (hazard ratio: 1.06, 95 % confidence interval: 1.03-1.08), PM2.5 (1.94, 1.52-2.48), NO2 (1.06, 1.02-1.10), and NOX (1.03, 1.01-1.04). However, no significant association was observed between PM10 or PM2.5-10 exposure and osteoporosis. Subjects with high air pollutant exposure levels and a high PRS had a noteworthy increase in osteoporosis risk compared to those with low air pollutant exposure levels and a low PRS. Air pollutants and genetic variants exerted additive effects on the risk of osteoporosis. Positive correlations were observed between osteoporosis and PM2.5 (P < 0.001), NO2 (P = 0.001), and NOx (P = 0.002) exposure. CONCLUSIONS Exposure to PM2.5, NO2 and NOx was associated with an increase in osteoporosis risk, and this effect was more pronounced in populations with high genetic risk. The association between PM2.5, NO2 and NOx exposure and osteoporosis is modified by genetic variations.
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Affiliation(s)
- Cheng Xu
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Zhenkun Weng
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Qian Liu
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Jin Xu
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, Nanjing Medical University, Nanjing, China; Department of Maternal, Child, and Adolescent Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Jingjia Liang
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Wenxiang Li
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, Nanjing Medical University, Nanjing, China
| | - Jia Hu
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Suzhou Institute of Advanced Study in Public Health, Gusu School, Nanjing Medical University, Suzhou, China; Suzhou Center for Disease Prevention and Control, Suzhou, China
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Yong Zhou
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
| | - Aihua Gu
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, Nanjing Medical University, Nanjing, China.
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Tavernier LJM, Vanpoucke T, Schrauwen I, Van Camp G, Fransen E. Targeted Resequencing of Otosclerosis Patients from Different Populations Replicates Results from a Previous Genome-Wide Association Study. J Clin Med 2022; 11:6978. [PMID: 36498562 PMCID: PMC9737413 DOI: 10.3390/jcm11236978] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 11/17/2022] [Accepted: 11/22/2022] [Indexed: 11/29/2022] Open
Abstract
Otosclerosis is one of the most common causes of hearing loss in young adults. It has a prevalence of 0.3-0.4% in the European population. Clinical symptoms usually occur between the second and fifth decade of life. Different studies have been performed to unravel the genetic architecture of the disease. Recently, a genome-wide association study (GWAS) identified 15 novel risk loci and replicated the regions of three previously reported candidate genes. In this study, seven candidate genes from the GWAS were resequenced using single molecule molecular inversion probes (smMIPs). smMIPs were used to capture the exonic regions and the 3' and 5' untranslated regions (UTR). Discovered variants were tested for association with the disease using single variant and gene-based association analysis. The single variant results showed that 13 significant variants were associated with otosclerosis. Associated variants were found in five of the seven genes studied here, including AHSG, LINC01482, MARK3, SUPT3H and RELN. Conversely, burden testing did not show a major role of rare variants in the disease. In conclusion, this study was able to replicate five out of seven candidate genes reported in the previous GWAS. This association is likely mainly driven by common variants.
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Affiliation(s)
- Lisse J. M. Tavernier
- Center of Medical Genetics, University of Antwerp & Antwerp University Hospital, 2650 Antwerp, Belgium
| | - Thomas Vanpoucke
- Center of Medical Genetics, University of Antwerp & Antwerp University Hospital, 2650 Antwerp, Belgium
| | - Isabelle Schrauwen
- Center for Statistical Genetics, Department of Neurology, Gertrude H. Sergievsky Center, Columbia University Medical Center, New York, NY 10032, USA
| | - Guy Van Camp
- Center of Medical Genetics, University of Antwerp & Antwerp University Hospital, 2650 Antwerp, Belgium
| | - Erik Fransen
- Center of Medical Genetics, University of Antwerp & Antwerp University Hospital, 2650 Antwerp, Belgium
- StatUA Center for Statistics, University of Antwerp, 2610 Antwerp, Belgium
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Golovchenko I, Aizikovich B, Golovchenko O, Reshetnikov E, Churnosova M, Aristova I, Ponomarenko I, Churnosov M. Sex Hormone Candidate Gene Polymorphisms Are Associated with Endometriosis. Int J Mol Sci 2022; 23:13691. [PMID: 36430184 PMCID: PMC9697627 DOI: 10.3390/ijms232213691] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 10/07/2022] [Accepted: 11/03/2022] [Indexed: 11/09/2022] Open
Abstract
The present study was designed to examine whether sex hormone polymorphisms proven by GWAS are associated with endometriosis risk. Unrelated female participants totaling 1376 in number (395 endometriosis patients and 981 controls) were recruited into the study. Nine single-nucleotide polymorphisms (SNPs) which GWAS correlated with circulating levels of sex hormones were genotyped using a TaqMan allelic discrimination assay. FSH-lowering, and LH- and testosterone-heightening polymorphisms of the FSHB promoter (allelic variants A rs11031002 and C rs11031005) exhibit a protective effect for endometriosis (OR = 0.60-0.68). By contrast, the TT haplotype loci that were GWAS correlated with higher FSH levels and lower LH and testosterone concentrations determined an increased risk for endometriosis (OR = 2.03). Endometriosis-involved epistatic interactions were found between eight loci of sex hormone genes (without rs148982377 ZNF789) within twelve genetic simulation models. In silico examination established that 8 disorder-related loci and 80 proxy SNPs are genome variants affecting the expression, splicing, epigenetic and amino acid conformation of the 34 genes which enrich the organic anion transport and secondary carrier transporter pathways. In conclusion, the present study showed that sex hormone polymorphisms proven by GWAS are associated with endometriosis risk and involved in the molecular pathophysiology of the disease due to their functionality.
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Affiliation(s)
- Ilya Golovchenko
- Department of Medical Biological Disciplines, Belgorod State University, 308015 Belgorod, Russia
| | - Boris Aizikovich
- Department of Fundamental Medicine, Novosibirsk State University, 630090 Novosibirsk, Russia
| | - Oleg Golovchenko
- Department of Obstetrics and Gynecology, Belgorod State University, 308015 Belgorod, Russia
| | - Evgeny Reshetnikov
- Department of Medical Biological Disciplines, Belgorod State University, 308015 Belgorod, Russia
| | - Maria Churnosova
- Department of Medical Biological Disciplines, Belgorod State University, 308015 Belgorod, Russia
| | - Inna Aristova
- Department of Medical Biological Disciplines, Belgorod State University, 308015 Belgorod, Russia
| | - Irina Ponomarenko
- Department of Medical Biological Disciplines, Belgorod State University, 308015 Belgorod, Russia
| | - Mikhail Churnosov
- Department of Medical Biological Disciplines, Belgorod State University, 308015 Belgorod, Russia
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Kolchina MA, Skripnikova IA, Meshkov AN, Kosmatova OV, Novikov VE, Isaykina OY, Kiseleva AV, Sotnikova EA, Vigodin VA, Pokrovskaya MS, Drapkina OM. Associations of bone mass and polygenic risk of osteoporosis with indicators of arterial wall condition. OSTEOPOROSIS AND BONE DISEASES 2022. [DOI: 10.14341/osteo12951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Background: The identification of genetic factors that are simultaneously responsible for the predisposition to the development of cardiovascular diseases (CVD) and osteoporosis (OP) is important for the prevention of both conditions.Aim: The aim of this study is to evaluate three genetic risk scales (GRS) that previously showed an association with bone mineral density (BMD) and fracture risk, as well as to study the associations of these GRS with vascular wall pathology.Materials and methods: 250 female outpatients (aged 45 to 69) were enrolled into a cross-sectional study. The intima-media thickness (IMT), the presence and number of atherosclerotic plaques (AP) were studied using duplex scanning. Pulse wave velocity (PWV), augmentation index (AI) were measured by applanation tonometry. Coronary vessels calcium deposits were registered by multispiral computed tomography (MSCT) using the Agatston calcium index (CI). The BMD of the spine, hip neck (HN) and proximal hip (PH) was measured using double energy x-ray absorptiometry. Bone resorption marker type-1 collagen C-terminal telopeptide (CTx) was assessed solid-phase enzyme immunoassay. The genetic study included DNA extraction from whole blood samples. Targeted sequencing was performed on the Nextseq550 sequencer (Illumina, USA). Statistical analysis was carried out using the SAS software package for Windows, version 9.0 (SAS Institute Inc., USA).Results: The chance of detecting low bone mass increased more than 4 times at values of IMT ≥0.9 mm (OR=4.17; 95%CI [1.2–14.4], p<0.02), 2.4 times in the presence of AP in the carotid arteries (OR=2.45; 95%CI [1.12–4.88], p><0.05), by 6.7 times with an Agatstone CI ≥ 100 units (OR=6.68; 95%CI [1.56–28.7], p><0.001), 1.4 times (OR=1.43; 95%CI [0.56–3.68], p><0.438) with a PWV ≥10 m/s, 1.2 times (OR=1.2; 95%CI [0.601–2.43], p><0.60) with increased AI ≥ 27%. According to multivariate linear regression analysis (adjusted for age, duration of postmenopause, marker of bone resorption CTx), a significant association of all GRS with BMD in all parts of the skeleton was revealed. Both univariate and multivariate regression models adjusted for several covariants (age, total cholesterol, systolic blood pressure) showed a reliable association of GRS62 with the presence of plaques and GRS63 — with coronary artery CI. Conclusion: The results of the study demonstrated the association of polygenic genetic risk of GRS-based OP with BMD and vascular wall status indicators in women in the peri and postmenopausal periods.>< 0.02), 2.4 times in the presence of AP in the carotid arteries (OR=2.45; 95%CI [1.12–4.88], p< 0.05), by 6.7 times with an Agatstone CI ≥ 100 units (OR=6.68; 95%CI [1.56–28.7], p< 0.001), 1.4 times (OR=1.43; 95%CI [0.56–3.68], p< 0.438) with a PWV ≥10 m/s, 1.2 times (OR=1.2; 95%CI [0.601–2.43], p<0.60) with increased AI ≥ 27%. According to multivariate linear regression analysis (adjusted for age, duration of postmenopause, marker of bone resorption CTx), a significant association of all GRS with BMD in all parts of the skeleton was revealed. Both univariate and multivariate regression models adjusted for several covariants (age, total cholesterol, systolic blood pressure) showed a reliable association of GRS62 with the presence of plaques and GRS63 — with coronary artery CI.>< 0.60) with increased AI ≥ 27%. According to multivariate linear regression analysis (adjusted for age, duration of postmenopause, marker of bone resorption CTx), a significant association of all GRS with BMD in all parts of the skeleton was revealed. Both univariate and multivariate regression models adjusted for several covariants (age, total cholesterol, systolic blood pressure) showed a reliable association of GRS62 with the presence of plaques and GRS63 — with coronary artery CI.Conclusion: The results of the study demonstrated the association of polygenic genetic risk of GRS-based OP with BMD and vascular wall status indicators in women in the peri and postmenopausal periods.
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Affiliation(s)
- M. A. Kolchina
- National Medical Research Center for Therapy and Preventive Medicine
| | - I. A. Skripnikova
- National Medical Research Center for Therapy and Preventive Medicine
| | - A. N. Meshkov
- National Medical Research Center for Therapy and Preventive Medicine
| | - O. V. Kosmatova
- National Medical Research Center for Therapy and Preventive Medicine
| | - V. E. Novikov
- National Medical Research Center for Therapy and Preventive Medicine
| | - O. Yu. Isaykina
- National Medical Research Center for Therapy and Preventive Medicine
| | - A. V. Kiseleva
- National Medical Research Center for Therapy and Preventive Medicine
| | - E. A. Sotnikova
- National Medical Research Center for Therapy and Preventive Medicine
| | - V. A. Vigodin
- National Medical Research Center for Therapy and Preventive Medicine
| | - M. S. Pokrovskaya
- National Medical Research Center for Therapy and Preventive Medicine
| | - O. M. Drapkina
- National Medical Research Center for Therapy and Preventive Medicine
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Using a Polygenic Score to Predict the Risk of Developing Primary Osteoporosis. Int J Mol Sci 2022; 23:ijms231710021. [PMID: 36077420 PMCID: PMC9456390 DOI: 10.3390/ijms231710021] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 08/30/2022] [Accepted: 08/30/2022] [Indexed: 11/28/2022] Open
Abstract
Osteoporosis (OP) is a multifactorial bone disease belonging to the metabolic osteopathies group. Using the polygenic score (PGS) approach, we combined the effects of bone mineral density (BMD) DNA loci, affecting osteoporosis pathogenesis, based on GEFOS/GENOMOS consortium GWAS meta-analysis. We developed models to predict the risk of low fractures in women from the Volga-Ural region of Russia with efficacy of 74% (AUC = 0.740; OR (95% CI) = 2.9 (2.353–3.536)), as well as the formation of low BMD with efficacy of 79% (AUC = 0.790; OR (95% CI) = 3.94 (2.993–5.337)). In addition, we propose a model that predicts fracture risk and low BMD in a comorbid condition with 85% accuracy (AUC = 0.850; OR (95% CI) = 6.6 (4.411–10.608)) in postmenopausal women.
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38
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Xiao X, Wu Q. Multiple polygenic scores improve bone mineral density prediction in an independent sample of Caucasian women. Postgrad Med J 2022; 98:670-674. [PMID: 34810269 DOI: 10.1136/postgradmedj-2021-139722] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 06/05/2021] [Indexed: 12/13/2022]
Abstract
PURPOSE OF THE STUDY To determine if multiple Genetic Risk Scores (GRSs) improve bone mineral density (BMD) prediction over single GRS in an independent sample of Caucasian women. STUDY DESIGN Based on summary statistics of four genome-wide association studies related to two osteoporosis-associated traits, namely BMD and heel quantitative ultrasound derived estimated BMD (eBMD), four GRSs were derived for 1205 individuals in the Genome-Wide Scan for Female Osteoporosis Gene Study. The effect of each GRS on BMD variation was assessed using multivariable linear regression, with conventional risk factors adjusted for. Next, the eBMD-related GRS that explained the most variance in BMD was selected to be entered into a multi-score model, along with the BMD-related GRS. Elastic net regularised regression was used to develop the multiscore model, which estimated the joint effect of two GRSs (GRS_BMD and GRS_eBMD) on BMD variation, after being adjusted for conventional risk factors. RESULTS With the same clinical risk factors having been adjusted for, the model that included GRS_BMD performed best by explaining 32.53% of the variance in BMD; the single-score model that included GRS_eBMD explained 34.03% of BMD variance. The model that includes both GRS_BMD and GRS_ eBMD, as well as the clinical risk factors, aggregately explained 35.05% in BMD variation. Compared with the single GRS models, the multiscore model explained significantly more variance in BMD. CONCLUSIONS The multipolygenic score model explained a considerable amount of BMD variation. Compared with single score models, multipolygenic score model provided significant improvement in explaining BMD variation.
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Affiliation(s)
- Xiangxue Xiao
- Nevada Institute of Personalized Medicine, College of Science, University of Nevada Las Vegas, Las Vegas, Nevada, USA.,Department of Epidemiology and Biostatistics, School of Public Health, University of Nevada Las Vegas, Las Vegas, Nevada, USA
| | - Qing Wu
- Nevada Institute of Personalized Medicine, College of Science, University of Nevada Las Vegas, Las Vegas, Nevada, USA .,Department of Epidemiology and Biostatistics, School of Public Health, University of Nevada Las Vegas, Las Vegas, Nevada, USA
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39
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Cho HW, Jin HS, Eom YB. FGFRL1 and FGF genes are associated with height, hypertension, and osteoporosis. PLoS One 2022; 17:e0273237. [PMID: 35980984 PMCID: PMC9387819 DOI: 10.1371/journal.pone.0273237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 08/04/2022] [Indexed: 11/21/2022] Open
Abstract
Hypertension and osteoporosis are two major disorders, which interact with each other. Specific genetic signals involving the fibroblast growth factor receptor-like 1 (FGFRL1) gene are related to high blood pressure and bone growth in giraffes. FGFRL1 is associated with cardiovascular system and bone formation. We performed an association study to investigate the role of FGFRL1 in hypertension, osteoporosis, and height determination in humans. In addition, we identified three kinds of phenotypes in fibroblast growth factor (FGF) genes and examined their association with the FGFRL1 gene. We identified 42 SNPs in the FGFRL1 gene associated with each trait. We then analyzed the potential functional annotation of each SNP. The FGFRL1 gene was found to be associated with height, hypertension, and osteoporosis, consistent with the results of a previous study. In addition, the FGF2, FGF4, FGF10, FGF18, and FGF22 genes were found to interact with the FGFRL1 gene. Our study suggests that both FGFRL1 and FGFRL1-related genes may determine the height and the prevalence of osteoporosis and hypertension in the Korean population.
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Affiliation(s)
- Hye-Won Cho
- Department of Medical Sciences, Graduate School, Soonchunhyang University, Asan, Chungnam, Republic of Korea
| | - Hyun-Seok Jin
- Department of Biomedical Laboratory Science, College of Life and Health Sciences, Hoseo University, Asan, Chungnam, Republic of Korea
| | - Yong-Bin Eom
- Department of Medical Sciences, Graduate School, Soonchunhyang University, Asan, Chungnam, Republic of Korea
- Department of Biomedical Laboratory Science, College of Medical Sciences, Soonchunhyang University, Asan, Chungnam, Republic of Korea
- * E-mail:
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40
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Rosa JT, Tarasco M, Gavaia PJ, Cancela ML, Laizé V. Screening of Mineralogenic and Osteogenic Compounds in Zebrafish—Tools to Improve Assay Throughput and Data Accuracy. Pharmaceuticals (Basel) 2022; 15:ph15080983. [PMID: 36015130 PMCID: PMC9412667 DOI: 10.3390/ph15080983] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 07/24/2022] [Accepted: 08/03/2022] [Indexed: 12/16/2022] Open
Abstract
Bone disorders affect millions of people worldwide and treatments currently available often produce undesirable secondary effects or have limited efficacy. It is therefore of the utmost interest for patients to develop more efficient drugs with reduced off-target activities. In the long process of drug development, screening and preclinical validation have recently gained momentum with the increased use of zebrafish as a model organism to study pathological processes related to human bone disorders, and the development of zebrafish high-throughput screening assays to identify bone anabolic compounds. In this review, we provided a comprehensive overview of the literature on zebrafish bone-related assays and evaluated their performance towards an integration into screening pipelines for the discovery of mineralogenic/osteogenic compounds. Tools available to standardize fish housing and feeding procedures, synchronize embryo production, and automatize specimen sorting and image acquisition/analysis toward faster and more accurate screening outputs were also presented.
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Affiliation(s)
- Joana T. Rosa
- Centre of Marine Sciences, University of Algarve, 8005-139 Faro, Portugal
- S2AQUA—Collaborative Laboratory, Association for a Sustainable and Smart Aquaculture, 8700-194 Olhão, Portugal
| | - Marco Tarasco
- Centre of Marine Sciences, University of Algarve, 8005-139 Faro, Portugal
| | - Paulo J. Gavaia
- Centre of Marine Sciences, University of Algarve, 8005-139 Faro, Portugal
- Faculty of Medicine and Biomedical Sciences, University of Algarve, 8005-139 Faro, Portugal
- GreenColab—Associação Oceano Verde, University of Algarve, 8005-139 Faro, Portugal
| | - M. Leonor Cancela
- Centre of Marine Sciences, University of Algarve, 8005-139 Faro, Portugal
- Faculty of Medicine and Biomedical Sciences, University of Algarve, 8005-139 Faro, Portugal
- Algarve Biomedical Center, University of Algarve, 8005-139 Faro, Portugal
| | - Vincent Laizé
- Centre of Marine Sciences, University of Algarve, 8005-139 Faro, Portugal
- S2AQUA—Collaborative Laboratory, Association for a Sustainable and Smart Aquaculture, 8700-194 Olhão, Portugal
- Correspondence:
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41
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Rokicki J, Kaufmann T, de Lange AMG, van der Meer D, Bahrami S, Sartorius AM, Haukvik UK, Steen NE, Schwarz E, Stein DJ, Nærland T, Andreassen OA, Westlye LT, Quintana DS. Oxytocin receptor expression patterns in the human brain across development. Neuropsychopharmacology 2022; 47:1550-1560. [PMID: 35347267 PMCID: PMC9205980 DOI: 10.1038/s41386-022-01305-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 03/04/2022] [Indexed: 12/31/2022]
Abstract
Oxytocin plays a vital role in social behavior and homeostatic processes, with animal models indicating that oxytocin receptor (OXTR) expression patterns in the brain influence behavior and physiology. However, the developmental trajectory of OXTR gene expression is unclear. By analyzing gene expression data in human post-mortem brain samples, from the prenatal period to late adulthood, we demonstrate distinct patterns of OXTR gene expression in the developing brain, with increasing OXTR expression along the course of the prenatal period culminating in a peak during early childhood. This early life OXTR expression peak pattern appears slightly earlier in a comparative macaque sample, which is consistent with the relative immaturity of the human brain during early life compared to macaques. We also show that a network of genes with strong spatiotemporal couplings with OXTR is enriched in several psychiatric illness and body composition phenotypes. Taken together, these results demonstrate that oxytocin signaling plays an important role in a diverse set of psychological and somatic processes across the lifespan.
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Affiliation(s)
- Jaroslav Rokicki
- grid.5510.10000 0004 1936 8921NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway ,grid.5510.10000 0004 1936 8921Department of Psychology, University of Oslo, Oslo, Norway ,grid.55325.340000 0004 0389 8485Centre of Research and Education in Forensic Psychiatry, Oslo University Hospital, Oslo, Norway
| | - Tobias Kaufmann
- grid.5510.10000 0004 1936 8921NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway ,grid.10392.390000 0001 2190 1447Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
| | - Ann-Marie G. de Lange
- grid.5510.10000 0004 1936 8921NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway ,grid.9851.50000 0001 2165 4204LREN, Centre for Research in Neurosciences - Department of Clinical Neurosciences, CHUV and University of Lausanne, Lausanne, Switzerland ,grid.4991.50000 0004 1936 8948Department of Psychiatry, University of Oxford, Oxford, UK
| | - Dennis van der Meer
- grid.5510.10000 0004 1936 8921NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway ,grid.5012.60000 0001 0481 6099School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Shahram Bahrami
- grid.5510.10000 0004 1936 8921NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway ,grid.5510.10000 0004 1936 8921Department of Psychology, University of Oslo, Oslo, Norway
| | - Alina M. Sartorius
- grid.5510.10000 0004 1936 8921NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway ,grid.5510.10000 0004 1936 8921Department of Psychology, University of Oslo, Oslo, Norway
| | - Unn K. Haukvik
- grid.5510.10000 0004 1936 8921NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway ,grid.55325.340000 0004 0389 8485Centre of Research and Education in Forensic Psychiatry, Oslo University Hospital, Oslo, Norway
| | - Nils Eiel Steen
- grid.5510.10000 0004 1936 8921NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Emanuel Schwarz
- grid.7700.00000 0001 2190 4373Central Institute of Mental Health, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Dan J. Stein
- grid.7836.a0000 0004 1937 1151SAMRC Unit on Risk & Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Terje Nærland
- grid.55325.340000 0004 0389 8485NevSom, Department of Rare Disorders, Oslo University Hospital, Oslo, Norway ,grid.5510.10000 0004 1936 8921KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Ole A. Andreassen
- grid.5510.10000 0004 1936 8921NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway ,grid.5510.10000 0004 1936 8921KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T. Westlye
- grid.5510.10000 0004 1936 8921NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway ,grid.5510.10000 0004 1936 8921Department of Psychology, University of Oslo, Oslo, Norway ,grid.5510.10000 0004 1936 8921KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Daniel S. Quintana
- grid.5510.10000 0004 1936 8921NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway ,grid.5510.10000 0004 1936 8921Department of Psychology, University of Oslo, Oslo, Norway ,grid.55325.340000 0004 0389 8485NevSom, Department of Rare Disorders, Oslo University Hospital, Oslo, Norway ,grid.5510.10000 0004 1936 8921KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
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Tang Y, Wei F, Yu M, Zhou H, Wang Y, Cui Z, Liu X. Absence of causal association between Vitamin D and bone mineral density across the lifespan: a Mendelian randomization study. Sci Rep 2022; 12:10408. [PMID: 35729194 PMCID: PMC9213555 DOI: 10.1038/s41598-022-14548-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 06/08/2022] [Indexed: 11/09/2022] Open
Abstract
Vitamin D deficiency is a candidate risk factor for osteoporosis, characterized by decreased bone mineral density (BMD). We performed this two-sample Mendelian randomization (MR) analysis to investigate the causal effect of vitamin D on BMD. We extracted 143 single-nucleotide polymorphisms from a recent GWAS on 417,580 participants of European ancestry as instrumental variables, and used summary statistics for BMD at forearm (n = 10,805), femoral neck (n = 49,988), lumbar spine (n = 44,731) and total-body of different age-stages (< 15, 15-30, 30-45, 45-60, > 60) (n = 67,358). We explored the direct effect of vitamin D on BMD with an adjusted body mass index (BMI) in a multivariable MR analysis. We found no support for causality of 25-hydroxyvitamin D on BMD at forearm, femoral neck, lumbar spine, and total-body BMD across the lifespan. There was no obvious difference between the total and direct effect of vitamin D on BMD after adjusting for BMI. Our MR analysis provided evidence that genetically determined vitamin D was not causally associated with BMD in the general population. Large-scale randomized controlled trials are warranted to investigate the role of vitamin D supplementation in preventing osteoporosis in the high-risk population.
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Affiliation(s)
- Yanchao Tang
- Department of Orthopaedics, Peking University Third Hospital, 49 North Garden Street, HaiDian District, Beijing, 100191, China. .,Beijing Key Laboratory of Spinal Disease Research and Engineering, Beijing, China. .,Research Center of Bone and Joint Precision Medicine, Ministry of Education, Beijing, China.
| | - Feng Wei
- Department of Orthopaedics, Peking University Third Hospital, 49 North Garden Street, HaiDian District, Beijing, 100191, China
| | - Miao Yu
- Department of Orthopaedics, Peking University Third Hospital, 49 North Garden Street, HaiDian District, Beijing, 100191, China
| | - Hua Zhou
- Department of Orthopaedics, Peking University Third Hospital, 49 North Garden Street, HaiDian District, Beijing, 100191, China
| | - Yongqiang Wang
- Department of Orthopaedics, Peking University Third Hospital, 49 North Garden Street, HaiDian District, Beijing, 100191, China
| | - Zhiyong Cui
- Health Science Center, Peking University, Beijing, China
| | - Xiaoguang Liu
- Department of Orthopaedics, Peking University Third Hospital, 49 North Garden Street, HaiDian District, Beijing, 100191, China. .,Health Science Center, Peking University, Beijing, China.
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43
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Oheim R, Tsourdi E, Seefried L, Beller G, Schubach M, Vettorazzi E, Stürznickel J, Rolvien T, Ehmke N, Delsmann A, Genest F, Krüger U, Zemojtel T, Barvencik F, Schinke T, Jakob F, Hofbauer LC, Mundlos S, Kornak U. Genetic Diagnostics in Routine Osteological Assessment of Adult Low Bone Mass Disorders. J Clin Endocrinol Metab 2022; 107:e3048-e3057. [PMID: 35276006 PMCID: PMC9202726 DOI: 10.1210/clinem/dgac147] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Indexed: 12/17/2022]
Abstract
CONTEXT Many different inherited and acquired conditions can result in premature bone fragility/low bone mass disorders (LBMDs). OBJECTIVE We aimed to elucidate the impact of genetic testing on differential diagnosis of adult LBMDs and at defining clinical criteria for predicting monogenic forms. METHODS Four clinical centers broadly recruited a cohort of 394 unrelated adult women before menopause and men younger than 55 years with a bone mineral density (BMD) Z-score < -2.0 and/or pathological fractures. After exclusion of secondary causes or unequivocal clinical/biochemical hallmarks of monogenic LBMDs, all participants were genotyped by targeted next-generation sequencing. RESULTS In total, 20.8% of the participants carried rare disease-causing variants (DCVs) in genes known to cause osteogenesis imperfecta (COL1A1, COL1A2), hypophosphatasia (ALPL), and early-onset osteoporosis (LRP5, PLS3, and WNT1). In addition, we identified rare DCVs in ENPP1, LMNA, NOTCH2, and ZNF469. Three individuals had autosomal recessive, 75 autosomal dominant, and 4 X-linked disorders. A total of 9.7% of the participants harbored variants of unknown significance. A regression analysis revealed that the likelihood of detecting a DCV correlated with a positive family history of osteoporosis, peripheral fractures (> 2), and a high normal body mass index (BMI). In contrast, mutation frequencies did not correlate with age, prevalent vertebral fractures, BMD, or biochemical parameters. In individuals without monogenic disease-causing rare variants, common variants predisposing for low BMD (eg, in LRP5) were overrepresented. CONCLUSION The overlapping spectra of monogenic adult LBMD can be easily disentangled by genetic testing and the proposed clinical criteria can help to maximize the diagnostic yield.
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Affiliation(s)
- Ralf Oheim
- Ralf Oheim, MD, Department of Osteology and Biomechanics, University Medical Center Hamburg-Eppendorf, Lottestraße 59, 22529 Hamburg, Germany.
| | - Elena Tsourdi
- Department of Medicine III, Technische Universität Dresden Medical Center, 01307 Dresden, Germany
- Center for Healthy Aging, Technische Universität Dresden Medical Center, 01307 Dresden, Germany
| | - Lothar Seefried
- Orthopedic Center for Musculoskeletal Research, Orthopedic Department, University of Würzburg, 97070 Würzburg, Germany
| | - Gisela Beller
- Centre of Muscle and Bone Research, Charité-Universitätsmedizin Berlin, 13353 Berlin, Germany
| | - Max Schubach
- Berlin Institute of Health at Charité – Universitätsmedizin Berlin, 13353 Berlin, Germany
| | - Eik Vettorazzi
- Department of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Julian Stürznickel
- Department of Osteology and Biomechanics, University Medical Center Hamburg-Eppendorf, 22529 Hamburg, Germany
- Department of Orthopaedics and Trauma Surgery, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Tim Rolvien
- Department of Osteology and Biomechanics, University Medical Center Hamburg-Eppendorf, 22529 Hamburg, Germany
- Department of Orthopaedics and Trauma Surgery, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Nadja Ehmke
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 13353 Berlin, Germany
| | - Alena Delsmann
- Department of Osteology and Biomechanics, University Medical Center Hamburg-Eppendorf, 22529 Hamburg, Germany
| | - Franca Genest
- Orthopedic Center for Musculoskeletal Research, Orthopedic Department, University of Würzburg, 97070 Würzburg, Germany
| | - Ulrike Krüger
- Core Facility Genomics, Berlin Institute of Health (BIH), 10178 Berlin, Germany
| | - Tomasz Zemojtel
- Core Facility Genomics, Berlin Institute of Health (BIH), 10178 Berlin, Germany
| | - Florian Barvencik
- Department of Osteology and Biomechanics, University Medical Center Hamburg-Eppendorf, 22529 Hamburg, Germany
| | - Thorsten Schinke
- Department of Osteology and Biomechanics, University Medical Center Hamburg-Eppendorf, 22529 Hamburg, Germany
| | - Franz Jakob
- Orthopedic Center for Musculoskeletal Research, Orthopedic Department, University of Würzburg, 97070 Würzburg, Germany
| | - Lorenz C Hofbauer
- Department of Medicine III, Technische Universität Dresden Medical Center, 01307 Dresden, Germany
- Center for Healthy Aging, Technische Universität Dresden Medical Center, 01307 Dresden, Germany
| | - Stefan Mundlos
- Institute of Medical Genetics and Human Genetics, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 13353 Berlin, Germany
- BIH Center for Regenerative Therapies, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10178 Berlin, Germany
- Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany
| | - Uwe Kornak
- Correspondence: Uwe Kornak, PhD, Institute of Human Genetics, Universitätsmedizin Göttingen, Heinrich-Düker-Weg 12, 37073 Göttingen, Germany.
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44
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Comprehensive characterization of posttranscriptional impairment-related 3'-UTR mutations in 2413 whole genomes of cancer patients. NPJ Genom Med 2022; 7:34. [PMID: 35654793 PMCID: PMC9163142 DOI: 10.1038/s41525-022-00305-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 05/05/2022] [Indexed: 11/09/2022] Open
Abstract
The 3' untranslated region (3'-UTR) is the vital element regulating gene expression, but most studies have focused on variations in RNA-binding proteins (RBPs), miRNAs, alternative polyadenylation (APA) and RNA modifications. To explore the posttranscriptional function of 3'-UTR somatic mutations in tumorigenesis, we collected whole-genome data from 2413 patients across 18 cancer types. Our updated algorithm, PIVar, revealed 25,216 3'-UTR posttranscriptional impairment-related SNVs (3'-UTR piSNVs) spanning 2930 genes; 24 related RBPs were significantly enriched. The somatic 3'-UTR piSNV ratio was markedly increased across all 18 cancer types, which was associated with worse survival for four cancer types. Several cancer-related genes appeared to facilitate tumorigenesis at the protein and posttranscriptional regulation levels, whereas some 3'-UTR piSNV-affected genes functioned mainly via posttranscriptional mechanisms. Moreover, we assessed immune cell and checkpoint characteristics between the high/low 3'-UTR piSNV ratio groups and predicted 80 compounds associated with the 3'-UTR piSNV-affected gene expression signature. In summary, our study revealed the prevalence and clinical relevance of 3'-UTR piSNVs in cancers, and also demonstrates that in addition to affecting miRNAs, 3'-UTR piSNVs perturb RBPs binding, APA and m6A RNA modification, which emphasized the importance of considering 3'-UTR piSNVs in cancer biology.
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45
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Genetic variation in WNT16 and its association with bone mineral density, fractures and osteoporosis in children with bone fragility. Bone Rep 2022; 16:101525. [PMID: 35535173 PMCID: PMC9077160 DOI: 10.1016/j.bonr.2022.101525] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 03/24/2022] [Accepted: 03/24/2022] [Indexed: 11/20/2022] Open
Abstract
Several genome-wide association studies (GWAS), GWAS meta-analyses, and mouse studies have demonstrated that wingless-related integration site 16 (WNT16) gene is associated with bone mineral density (BMD), cortical bone thickness, bone strength and fracture risk. Practically no data exist regarding the significance of WNT16 in childhood-onset osteoporosis and related fractures. We hypothesized that pathogenic variants and genetic variations in WNT16 could explain skeletal fragility in affected children. We screened the WNT16 gene by Sanger sequencing in three pediatric cohorts: 35 with primary osteoporosis, 59 with multiple fractures, and in 95 healthy controls. Altogether, we identified 12 variants in WNT16. Of them one was a rare 5′UTR variant rs1386898215 in genome aggregate and medical trans-omic databases (GnomAD, TOPMED; minor allele frequency (MAF) 0.00 and 0.000008, respectively). One variant rs1554366753, overrepresented in children with osteoporosis (MAF = 0.06 vs healthy controls MAF = 0.01), was significantly associated with lower BMD. This variant was found associated with increased WNT16 gene expression at mRNA level in fibroblast cultures. None of the other identified variants were rare (MAF < 0.001) or deemed pathogenic by predictor programs. WNT16 may play a role in childhood osteoporosis but genetic WNT16 variation is not a common cause of skeletal fragility in childhood. No pathogenic WNT16 variants were found associated with pediatric osteoporosis or fracture-prone patients Altogether, twelve WNT16 variants were found in pediatric osteoporosis or fracture-prone patients The genetic variation rs1554366753 in the WNT16 gene is associated with bone mineral density and primary osteoporosis
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46
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Salek Ardestani S, Zandi MB, Vahedi SM, Mahboudi H, Mahboudi F, Meskoob A. Detection of common copy number of variation underlying selection pressure in Middle Eastern horse breeds using whole-genome sequence data. J Hered 2022; 113:421-430. [PMID: 35605262 DOI: 10.1093/jhered/esac027] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Accepted: 05/21/2022] [Indexed: 11/14/2022] Open
Abstract
Dareshouri, Arabian, and Akhal-Teke are three Middle Eastern horse breeds that have been selected for endurance and adaptation to harsh climates. Deciphering the genetic characteristics of these horses by tracing selection footprints and copy number of variations will be helpful in improving our understanding of equine breeds' development and adaptation. For this purpose, we sequenced the whole-genome of four Dareshouri horses using Illumina Hiseq panels and compared them with publicly available whole-genome sequences of Arabian (n=3) and Akhal-Teke (n=3) horses . Three tests of FLK, hapFLK, and pooled heterozygosity were applied using a sliding window (window size=100kb, step size=50kb) approach to detect putative selection signals. Copy number variation analysis was applied to investigate copy number of variants (CNVs), and the results were used to suggest selection signatures involving CNVs. Whole-genome sequencing demonstrated 8,837,950 single nucleotide polymorphisms (SNPs) in autosomal chromosomes. We suggested 58 genes and three quantitative trait loci (QTLs), including some related to horse gait, insect bite hypersensitivity, and withers height, based on selective signals detected by adjusted p-value of Mahalanobis distance based on the rank-based P-values (Md-rank-P) method. We proposed 12 genomic regions under selection pressure involving CNVs which were previously reported to be associated with metabolism energy (SLC5A8), champagne dilution in horses (SLC36A1), and synthesis of polyunsaturated fatty acids (FAT2). Only 10 Middle Eastern horses were tested in this study; therefore, the conclusions are speculative. Our findings are useful to better understanding the evolution and adaptation of Middle Eastern horse breeds.
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Affiliation(s)
- Siavash Salek Ardestani
- Department of Animal Science, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | | | - Seyed Milad Vahedi
- Department of Animal Science and Aquaculture, Dalhousie University, Truro, Canada
| | - Hossein Mahboudi
- Department of Biotechnology, School of Pharmacy, Alborz University of Medical Sciences, Karaj, Iran
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47
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Kasher M, Freidin MB, Williams FM, Cherny SS, Malkin I, Livshits G. Shared Genetic Architecture Between Rheumatoid Arthritis and Varying Osteoporotic Phenotypes. J Bone Miner Res 2022; 37:440-453. [PMID: 34910834 DOI: 10.1002/jbmr.4491] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 11/19/2021] [Accepted: 12/08/2021] [Indexed: 11/08/2022]
Abstract
Rheumatoid arthritis (RA) and low bone mineral density (BMD), an indicator of osteoporosis (OP), appear epidemiologically associated. Shared genetic factors may explain this association. This study aimed to investigate the presence of pleiotropy to clarify the potential genetic association between RA and OP. We examined BMDs at varying skeletal sites reported in UK Biobank as well as OP fracture acquired from the Genetic Factors for Osteoporosis (GEFOS) Consortium and the TwinsUK study. PRSice-2 was used to assess the potential shared genetic overlap between RA and OP. The presence of pleiotropy was examined using colocalization analysis. PRSice-2 revealed that RA was significantly associated with OP fracture (β = 351.6 ± 83.9, p value = 2.76E-05), total BMD (β = -1763.5 ± 612.8, p = 4.00E-03), spine BMD (β = -919.8 ± 264.6, p value = 5.09E-04), and forearm BMD (β = -66.09 ± 31.40, p value = 3.53E-02). Through colocalization analysis, the same causal genetic variants, associated with both RA and OP, were apparent in 12 genes: PLCL1, BOLL, AC011997.1, TNFAIP3, RP11-158I9.1, CDK6, CHCHD4P2, RP11-505C13.1, PHF19, TRAF1, C5, and C11orf49 with moderate posterior probabilities (>50%). Pleiotropy is involved in the association between RA and OP phenotypes. These findings contribute to the understanding of disease mechanisms and provide insight into possible therapeutic advancements and enhanced screening measures. © 2021 American Society for Bone and Mineral Research (ASBMR).
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Affiliation(s)
- Melody Kasher
- Human Population Biology Research Unit, Department of Anatomy and Anthropology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Maxim B Freidin
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK
| | - Frances Mk Williams
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK
| | - Stacey S Cherny
- Human Population Biology Research Unit, Department of Anatomy and Anthropology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,Department of Epidemiology and Preventive Medicine, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Ida Malkin
- Human Population Biology Research Unit, Department of Anatomy and Anthropology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Gregory Livshits
- Human Population Biology Research Unit, Department of Anatomy and Anthropology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.,Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK.,Adelson Medical School, Ariel University, Ariel, Israel
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48
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Xue JY, Grigelioniene G, Wang Z, Nishimura G, Iida A, Matsumoto N, Tham E, Miyake N, Ikegawa S, Guo L. SLC4A2 Deficiency Causes a New Type of Osteopetrosis. J Bone Miner Res 2022; 37:226-235. [PMID: 34668226 DOI: 10.1002/jbmr.4462] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 10/04/2021] [Accepted: 10/07/2021] [Indexed: 02/05/2023]
Abstract
Osteopetrosis is a group of rare inherited skeletal disorders characterized by a marked increase in bone density due to deficient bone resorption. Pathogenic variants in several genes involved in osteoclast differentiation and/or function have been reported to cause osteopetrosis. Solute carrier family 4 member 2 (SLC4A2, encoding anion exchanger 2) plays an important role in osteoclast differentiation and function by exchange of Cl- with HCO3- . Biallelic Slc4a2 loss-of-function mutations in mice and cattle lead to osteopetrosis with osteoclast deficiency; however, pathogenic SLC4A2 variants in humans have not been reported. In this study, we describe a patient with autosomal recessive osteopetrosis due to biallelic pathogenic variants in SLC4A2. We identified novel compound heterozygous variants in SLC4A2 (NM_003040.4: c.556G>A [p.A186T] and c.1658T>C [p.V553A]) by exome sequencing. The measurement of intracellular Cl- showed that the variants decrease the anion exchange activity of SLC4A2. The impact of the variants on osteoclast differentiation was assessed by a gene knockout-rescue system using a mouse macrophage cell line, RAW 264.7. The Slc4a2-knockout cells show impaired osteoclastogenesis, which was rescued by the wild-type SLC4A2, but not by the mutant SLC4A2s. Immunofluorescence and pit assay revealed that the mutant SLC4A2s leads to abnormal podosome belt formation with impaired bone absorption. This is the first report on an individual affected by SLC4A2-associated osteopetrosis (osteopetrosis, Ikegawa type). With functional studies, we prove that the variants lead to SLC4A2 dysfunction, which altogether supports the importance of SLC4A2 in human osteoclast differentiation. © 2021 American Society for Bone and Mineral Research (ASBMR).
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Affiliation(s)
- Jing-Yi Xue
- Laboratory for Bone and Joint Diseases, RIKEN Center for Integrative Medical Sciences, Tokyo, Japan.,Department of Human Genetics, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Giedre Grigelioniene
- Department of Molecular Medicine and Surgery, Karolinska Institutet, and Clinical Genetics, Karolinska University Laboratory, Karolinska University Hospital, Stockholm, Sweden
| | - Zheng Wang
- Laboratory for Bone and Joint Diseases, RIKEN Center for Integrative Medical Sciences, Tokyo, Japan.,Department of Medical Genetics, Institute of Basic Medical Sciences, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Gen Nishimura
- Laboratory for Bone and Joint Diseases, RIKEN Center for Integrative Medical Sciences, Tokyo, Japan
| | - Aritoshi Iida
- Laboratory for Bone and Joint Diseases, RIKEN Center for Integrative Medical Sciences, Tokyo, Japan.,Department of Clinical Genome Analysis, Medical Genome Center, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Naomichi Matsumoto
- Department of Human Genetics, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Emma Tham
- Department of Molecular Medicine and Surgery, Karolinska Institutet, and Clinical Genetics, Karolinska University Laboratory, Karolinska University Hospital, Stockholm, Sweden
| | - Noriko Miyake
- Department of Human Genetics, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Shiro Ikegawa
- Laboratory for Bone and Joint Diseases, RIKEN Center for Integrative Medical Sciences, Tokyo, Japan
| | - Long Guo
- Laboratory for Bone and Joint Diseases, RIKEN Center for Integrative Medical Sciences, Tokyo, Japan
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49
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Stover DA, Housman G, Stone AC, Rosenberg MS, Verrelli BC. Evolutionary Genetic Signatures of Selection on Bone-Related Variation within Human and Chimpanzee Populations. Genes (Basel) 2022; 13:183. [PMID: 35205228 PMCID: PMC8871609 DOI: 10.3390/genes13020183] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 01/19/2022] [Accepted: 01/19/2022] [Indexed: 02/06/2023] Open
Abstract
Bone strength and the incidence and severity of skeletal disorders vary significantly among human populations, due in part to underlying genetic differentiation. While clinical models predict that this variation is largely deleterious, natural population variation unrelated to disease can go unnoticed, altering our perception of how natural selection has shaped bone morphologies over deep and recent time periods. Here, we conduct the first comparative population-based genetic analysis of the main bone structural protein gene, collagen type I α 1 (COL1A1), in clinical and 1000 Genomes Project datasets in humans, and in natural populations of chimpanzees. Contrary to predictions from clinical studies, we reveal abundant COL1A1 amino acid variation, predicted to have little association with disease in the natural population. We also find signatures of positive selection associated with intron haplotype structure, linkage disequilibrium, and population differentiation in regions of known gene expression regulation in humans and chimpanzees. These results recall how recent and deep evolutionary regimes can be linked, in that bone morphology differences that developed among vertebrates over 450 million years of evolution are the result of positive selection on subtle type I collagen functional variation segregating within populations over time.
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Affiliation(s)
- Daryn A. Stover
- School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA;
- Arizona State University at Lake Havasu, Lake Havasu, AZ 86403, USA
| | - Genevieve Housman
- Section of Genetic Medicine, University of Chicago, Chicago, IL 60637, USA;
| | - Anne C. Stone
- School of Human Evolution and Social Change, Arizona State University, Tempe, AZ 85287, USA;
| | - Michael S. Rosenberg
- Center for Biological Data Science, Virginia Commonwealth University, Richmond, VA 23284, USA;
| | - Brian C. Verrelli
- School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA;
- Center for Biological Data Science, Virginia Commonwealth University, Richmond, VA 23284, USA;
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50
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Martínez-Gil N, Ugartondo N, Grinberg D, Balcells S. Wnt Pathway Extracellular Components and Their Essential Roles in Bone Homeostasis. Genes (Basel) 2022; 13:genes13010138. [PMID: 35052478 PMCID: PMC8775112 DOI: 10.3390/genes13010138] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 01/10/2022] [Accepted: 01/11/2022] [Indexed: 12/11/2022] Open
Abstract
The Wnt pathway is involved in several processes essential for bone development and homeostasis. For proper functioning, the Wnt pathway is tightly regulated by numerous extracellular elements that act by both activating and inhibiting the pathway at different moments. This review aims to describe, summarize and update the findings regarding the extracellular modulators of the Wnt pathway, including co-receptors, ligands and inhibitors, in relation to bone homeostasis, with an emphasis on the animal models generated, the diseases associated with each gene and the bone processes in which each member is involved. The precise knowledge of all these elements will help us to identify possible targets that can be used as a therapeutic target for the treatment of bone diseases such as osteoporosis.
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